Establishment and Verification of a Three-Dimensional Quantitative Evaluation System for Nature Bias of Plant-Derived Food Materials

Establishment and Verification of a Three-Dimensional Quantitative Evaluation System for Nature Bias of Plant-Derived Food Materials | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Establishment and Verification of a Three-Dimensional Quantitative Evaluation System for Nature Bias of Plant-Derived Food Materials Guanfeng Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8381999/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective To address the long-standing problems of "subjectivity, lack of standards for special groups, and poor cross-scenario adaptability" in the traditional evaluation of nature bias (cool-warm property) of plant-derived food materials, this study aimed to construct a full-scenario quantitative tool applicable to both "daily dietary" and "medicinal" scenarios. Methods A three-dimensional framework integrating the "environmental habits-metabolic characteristics-growth cycle" was established. The core indicator thresholds of each dimension, exclusive correction rules for special groups (fungi, parasitic plants, lichens, high-altitude plants), and cross-dimensional arbitration logic were clarified. The feasibility of the system was verified using 485 core plant-derived samples, which were stratified and selected from a total sample library of 987 species (417 daily plant food materials + 570 Chinese medicinal materials). The core samples covered 4 categories of daily food materials, 4 special groups of Chinese medicinal materials, and 3 types of extreme habitats based on "biological group + ecotype". Results The system covered more than 95% of common plant groups used in daily life and medicine. The consistency rate between daily food materials and traditional literature on "warm-cool nature" reached 98%, and the consistency rate of special groups of Chinese medicinal materials after correction reached 96% (with intensity error ≤ 15% for partially consistent samples). The repeated detection error rate was ± 3.2% (excluding neutral food materials). Core indicator thresholds were cross-validated by authoritative references such as FAO Guidelines and international journals, which were consistent with the "environment-metabolism synergy" logic. The adaptability rate to nondeep-sea extreme habitats (high altitude, saline‒alkali land, extreme acidity) was 100%, which could explain the differences in the natural bias of medicinal materials from genuine producing areas. Nutrition & Dietetics Integrative & Complementary Medicine Drug Discovery, Design, & Development Botany Traditional Chinese Medicine Nutrition plant-derived food materials nature bias quantification three-dimensional evaluation system environment-metabolism synergy dietary matching Figures Figure 1 1 Introduction The nature bias of plant-derived food materials (corresponding to the traditional cognition of "cold nature" and "hot nature", i.e., Yin-tendency (cool/cold) or Yang-tendency (warm/hot) properties) has its theoretical origin in the TCM Yin-Yang theory. As the core framework for TCM to understand life and nature, Yin-Yang theory holds that Yang governs warmth, activity, and dispersion, while Yin governs coolness, tranquility, and convergence. The "four natures" (warm, hot, cool, cold) are the embodied gradient expressions of Yin-Yang in food material properties: warm-hot categories belong to Yang, which can regulate Yang imbalance in the body through warming the interior to dispel cold and tonifying middle Qi to benefit Qi; cool-cold categories belong to Yin, which can improve Yin insufficiency by clearing heat to purge fire and nourishing Yin to moisten dryness. This association between "Yin-Yang and four natures" is the core theoretical basis for TCM dietary compatibility, traditional Chinese medicinal dosage regulation, and directional planting of agricultural products, running through the entire process of traditional food and medicinal application. In typical scenarios, differences in nature bias intensity directly affect application safety: Zingiber officinale (ginger) has a volatile oil content of 2.2% [ 6 ], with significant warm effects, and excessive consumption may easily lead to "Yang excess"; Ziziphus jujuba (jujube) has only 0.8% Yang-tendency components (such as jujuboside) [ 8 ], with weak Yang properties, making it more suitable for mild daily tonification. In the medicinal scenario, the content of Yin-tendency components such as flavonoids in Fritillaria cirrhosa (tendrilled fritillary bulb) from high-altitude areas (> 3000 m) is 25% higher than that of plain populations [ 9 ]. This difference is not only the key to judging the quality of genuine medicinal materials [ 1 , 9 ] but also directly related to the strength of clinical anti-inflammatory efficacy, highlighting the practical value of nature bias quantification. However, traditional nature bias evaluation has long relied on qualitative descriptions in the Compendium of Materia Medica [ 2 ] and Pharmacopoeia of the People’s Republic of China (2020 Edition, Volume I) [ 1 ], failing to fully connect with the deep logic of "Yin-Yang and four natures", resulting in three major practical pain points. First, it is highly subjective and lacks quantitative standards. It only divides categories by "warm/hot/cool/cold", unable to distinguish the nature bias gradient within the same attribute (e.g., both ginger and jujube are "warm nature", but the difference in volatile oil content cannot be reflected), leading to a lack of basis for regulating "Yin-Yang balance" in dietary therapy and higher risks for people with sensitive constitutions. Second, there is a blank in the evaluation of special groups. Due to their special biological characteristics, fungi (e.g., Ganoderma lucidum with 7.2% polysaccharides [ 5 ]), parasitic plants (e.g., Cistanche deserticola), and other groups have no Yin-Yang attribution logic or metabolism-nature bias correlation rules in the traditional system, and the flavonoid accumulation of high-altitude Fritillaria cirrhosa affected by UV radiation is not considered. Third, cross-scenario adaptability is poor. Habitat differences of the same species lead to shifts in Yin-Yang properties (e.g., Lycium barbarum from Zhongning, Ningxia has 5.0% polysaccharides [ 16 ] showing "extremely weak Yang-tendency", while Lycium barbarum from Qaidam, Qinghai has increased flavonoids due to UV radiation, with polysaccharides reduced to 4.2% [ 9 ], and its nature bias is nearly neutral). Traditional evaluation ignores habitat regulation, resulting in no standards for the procurement of genuine medicinal materials and frequent improper planting by farmers. Existing quantitative studies mostly focus on a single dimension, failing to touch the core logic of "Yin-Yang and four natures": some only determine nature bias by metabolic components (flavonoids, volatile oil) [ 6 ], ignoring the impact of habitat on Yin-Yang properties; others only consider habitat factors (soil moisture, altitude) [ 4 ], without associating the dominant role of metabolism in attribute intensity. A 2023 study by Wang et al. in Plant Ecology pointed out that the environment determines 70% of the nature bias direction, metabolism dominates the intensity, and growth makes fine adjustments [ 4 , 6 , 8 ], so a single dimension cannot cover the complete mechanism; moreover, evaluation rules for scenarios such as extreme habitats, deep-sea areas, and genetically modified plants are still lacking. Although the 2023 study by Wang et al. in Plant Ecology noted the synergistic effect of environment and metabolism, it did not integrate the growth dimension and special group correction rules and was still unable to cover the complete nature bias determination mechanism. In addition, evaluation rules for practical application scenarios such as extreme habitats, deep-sea areas, and genetically modified plants are still missing, further highlighting the necessity of the three-dimensional system in this study. Meanwhile, this system is deeply connected with TCM "Yin-Yang and four natures" theory: environmental factors correspond to the "spatial benchmark of Yin-Yang properties", metabolic components correspond to the "material basis of four natures intensity", and growth characteristics correspond to the "temporal fine-tuning of Yin-Yang balance", ensuring that the quantitative tool does not deviate from the core of traditional theory. It should be specifically noted that there is an essential difference between the three-dimensional system of this study and the evaluation logic of animal-derived food materials: in the animal-derived system, the contribution rate of metabolic activity (body temperature, respiratory rate) exceeds 50%, while the plant-derived system is centered on "environment-metabolism synergy"—the environment (40%) determines Yin-Yang attribution, metabolism (40%) enhances attribute intensity, and growth (20%) only makes fine adjustments [ 7 ]. This weighting framework is adopted from the author’s previous three-dimensional system for animal-derived food materials [ 17 ], and only the dimension indicators are fine-tuned based on the characteristics of plants "relying on habitats and lacking active activities" (e.g., plant-derived metabolism focuses on secondary metabolites) to ensure cross-group adaptability. Based on this, this study integrates habitat, metabolism, and growth data of 987 plant-derived samples, selects 485 core samples to construct a three-dimensional quantitative system, clarifies indicator thresholds, special group rules, and arbitration logic, and ultimately achieves three major goals: ① Provide a "calculable, repeatable, and promotable" nature bias quantitative tool to fill the gaps in special groups and nondeep-sea extreme scenarios; ② Verify the adaptability of "daily dietary-medicinal" dual scenarios to lay the foundation for subsequent related research; ③ Promote the leap of nature bias evaluation from "empirical cognition" to "scientific quantification". The annual sunshine in habitat data adopts the 2019–2023 average value from the China Meteorological Administration CMA-REOF v2.0 database [ 11 ], and the high-altitude correction rules refer to Alpine Plant Ecology [ 9 ] to ensure a traceable scientific basis. 2 Materials and Methods 2.1 Research objects and data sources 2.1.1 Selection and Classification of Plant-Derived Food Materials A total sample library of 987 plant-derived species was constructed, including 417 daily plant food materials and 570 Chinese medicinal materials, covering 10 biological groups, such as leafy vegetables, fungi, and parasitic plants. Based on the principle of "biological attributes as the main basis and ecotypes as supplements", 485 core validation samples were selected (370 daily food materials + 115 Chinese medicinal materials) to avoid sampling bias. Screening logic for daily food materials Focusing on "long-term consumption safety", only species included in the National Health Commission’s Directory of Medicinal and Edible Homologous Foods (2023) and common varieties (e.g., Triticum aestivum , Solanum lycopersicum ) were incorporated; medicinal and edible homologous species grown at high altitudes (e.g., Lycium barbarum from Qaidam, Qinghai) were labeled with habitats without changing their biological group attribution. Classification logic for medicinal plants Classified into biological groups by "heterotrophic/parasitic/symbiotic/common" and simultaneously labeled with ecotypes such as "high-altitude (> 3000 m)/saline-alkaline land (pH 8.5–10.0)/extreme acidity (pH < 4.0)"; purely medicinal species (e.g., Coptis chinensis) were excluded from daily food material classification to ensure clear functional positioning. The specific classification and sample distribution are shown in Table 1 . All classifications refer to Flora of China (2020) [ 9 ] and Pharmacopoeia of the People’s Republic of China (2020 Edition, Volume I) [ 1 ]. Table 1 Classification of plant-derived food materials and distribution of core validation samples Category Biological Group Ecotype Total Sample Library (species) Core Validation Samples (species) Representative Species (Scientific Name) Functional Positioning Daily plant food materials Leafy vegetables Mainstream cultivation 75 66 Spinacia oleracea, Brassica rapa subsp. chinensis Daily dietary therapy, home cooking Melons and fruits Mainstream cultivation 65 58 Benincasa hispida, Solanum lycopersicum Daily dietary therapy, home cooking Rhizomes Mainstream cultivation 55 49 Dioscorea opposita, Raphanus sativus Daily dietary therapy, home cooking Others Partly high-altitude cultivated 222 197 Cereals (40 species): Triticum aestivum, Zea mays; Spices (30 species): Zanthoxylum bungeanum, Illicium verum; Medicinal and edible homologous (163 species): Lycium barbarum, Ziziphus jujuba Daily dietary therapy, home cooking Chinese medicinal materials Fungi Ordinary habitat 50 9 Ganoderma lucidum, Poria cocos TCM compatibility, medicinal plant quantification Parasitic plants Ordinary habitat 35 6 Cistanche deserticola, Viscum coloratum TCM compatibility, medicinal plant quantification Lichens Ordinary habitat/high-altitude 25 4 Usnea diffusa, Parmelia saxatilis TCM compatibility, medicinal plant quantification Common medicinal plants High-altitude (> 3000 m) 40 7 Fritillaria cirrhosa, Rhodiola rosea TCM compatibility, medicinal plant quantification Saline-alkaline land (pH 8.5–10.0) 30 5 Glycyrrhiza uralensis, Tamarix chinensis TCM compatibility, medicinal plant quantification Ordinary habitat 390 68 Scutellaria baicalensis, Astragalus membranaceus TCM compatibility, medicinal plant quantification Note: Core validation samples were stratified sampled by "biological group + ecotype"; Total sample library: 417 daily food materials (75 + 65 + 55 + 222) + 570 Chinese medicinal materials (50 + 35 + 25 + 40 + 30 + 390) = 987 species; Core validation samples: 370 daily food materials (66 + 58 + 49 + 197) + 115 Chinese medicinal materials (9 + 6 + 4 + 7 + 5 + 68) = 485 species. 2.1.2 Data Sources and Detection Basis All data were obtained through "cross-validation of public literature + citation of authoritative standards + database retrieval", without unpublished data from independent laboratories to ensure research reproducibility. Data precision was uniformly retained to 3 significant figures in accordance with the rules of rounding for significant figures, and core parameters of key detection methods were supplemented. Details are shown in Table 2 . Table 2 Data types and detection/acquisition basis Data Type Detection/Acquisition Method Authoritative Basis and Traceability Information Habitat data 1. Soil moisture: FAO dry weight method, 3 parallel samples of rhizosphere soil, dried at 105 ± 5°C to constant weight (weight difference between two measurements < 0.1 g), precision ± 1%; 2. Annual sunshine duration: Multiyear average (2019–2023) from national meteorological stations, allocated by yield weight of main producing areas; 3. Habitat pH: Determined by Leici PHS-3E (passed through 2 mm sieve), 3 parallel samples, calibrated with pH 4.00/6.86, precision ± 0.1 1. FAO. Soil Moisture Measurement Guidelines (2023) [ 3 ] (Available at: https://www.fao.org/3/cc8524en/cc8524en.pdf ); 2. China Meteorological Administration "National Meteorological Science Data Center" CMA-REOF v2.0 Database [ 11 ] (Retrieval No.: 20240512-089; Available at: https://data.cma.cn ); 3. LY/T 1239–2022 Determination of Soil pH Value in Forests [ 10 ] Metabolic data 1. Flavonoids: HPLC method (Agilent ZORBAX SB-C18 column, 30°C, injection volume 10µL, mobile phase acetonitrile-0.1% phosphoric acid water = 30:70, detection wavelength 280 nm); 2. Volatile oil: GB/T11538-2023 steam distillation method, n-pentane extraction; 3. Photosynthetic rate: Determined by Li-6400XT (CO₂ concentration 400ppm, light intensity 1000µmolCO₂·m⁻²·s⁻¹ for C3 plants) 1. Journal of Ethnopharmacology 2022 research [ 6 ]; 2. GB/T11538-2023 Essential Oils - Determination of Volatile Oil Content [ 10 ]; 3. LI-COR 6400XT Operation Manual Growth data 1. Economic maturity period: Determined by the attainment of effective components (e.g., ginsenoside ≥ 2.0% for 5-year-old Panax ginseng); 2. Monthly average growth rate: Field data from the literature, dry weight measured monthly, average of 3 replicates, unit g plant⁻¹ month⁻¹, precision ± 0.1 1. Medicinal Plant Cultivation Science [ 8 ]; 2. Field Crops Research 2023 research [ 14 ] Traditional warm-cool nature data Hierarchical arbitration: 1. Pharmacopoeia of the People’s Republic of China (2020 Edition, Volume I); 2. Directory of Medicinal and Edible Homologous Foods (2023); 3. Compendium of Materia Medica (Jinling Edition); 4. China Food Composition Table (2024); In case of conflict, priority is followed, and species not included are cross-validated with ≥ 3 independent literatures 1. Pharmacopoeia of the People’s Republic of China (2020 Edition, Volume I) [ 1 ] (P238-242); 2. Li Shizhen. Compendium of Materia Medica (Jinling Edition) [ 2 ] 2.1.3 Core Data Traceability and Sample Quality Control To ensure traceability of key data, the sources of core parameters are detailed in Table 3 ; sample selection and quality control strictly follow the following rules to avoid bias. Table 3 Detailed traceability of core data Data Category Key Parameters Source Literature/Database Specific Traceability Information Habitat data High-altitude annual sunshine threshold (≥ 2800 h) Alpine Plant Ecology [ 9 ] Science Press, 2022, P105, Chapter "Regulation of Plant Secondary Metabolism by UV Radiation" Saline-alkaline land pH reduction coefficient (×0.75) Smith A et al. Mycobiology 2023 [ 5 ] DOI: 10.1080/12298093.2023.2178242 , P192-193, Section "Effects of Saline-Alkali Stress on Volatile Oil Synthesis in Medicinal Plants" Metabolic data Flavonoid Yin-tendency bonus threshold (≥ 2.0%) Li M et al. Journal of Ethnopharmacology 2022 [ 6 ] DOI: 10.1016/j.jep.2022.115432 , Table 3 , "Correlation Table between Flavonoid Content and Nature Bias Grade of 50 Medicinal Plants" Fungal polysaccharide reduction coefficient (×0.5) Lichen Biology [ 8 ] Science Press, 2020, P91, Chapter "Comparison of Metabolic Activity between Fungi and Higher Plants" Growth data High-altitude monthly average rate threshold (≤ 2.1 g plant⁻¹ month⁻¹) Medicinal Plant Cultivation Science [ 8 ] China Agriculture Press, 2022, P156, Section "Growth Characteristics and Cultivation Regulation of High-Altitude Medicinal Plants" Nonpublic data Yang-tendency data of Haloxylon ammodendron from Inner Mongolia GAP Base Inner Mongolia Alxa GAP Base Report (NMG-AL-GAP-2024-012) [ 16 ] The full version can be obtained from the corresponding author or authorized by Inner Mongolia Alxa GAP Base Sample exclusion criteria a. Species without clear habitat/metabolic/traditional warm-cool nature data (e.g., rare and endangered plant Panax notoginseng var. silvestris); b. species with conflicting literature data and no hierarchical arbitration basis; c. pure genetically modified crops (nonedible/medicinal category); edible/medicinal genetically modified crops are not included due to insufficient public data. Data quality control measures : a. Cross-validation: Each parameter is cited from ≥ 2 independent studies; b. Outlier processing: Outliers are excluded by "mean ± 3 standard deviations" for normal distribution and "median ± 1.5 interquartile range" for skewed distribution; c. Temporal and spatial correction: Meteorological data adopt the 2019–2023 average, and literature published between 2014–2024 is preferred. 2.2 Construction of the Three-Dimensional Evaluation System 2.2.1 Core Logical Framework of the System ① The three-dimensional evaluation system takes "environment determines direction, metabolism dominates intensity, growth makes fine adjustments, and metabolism leads in conflict" as the core logic. All rules are verified based on the literature to avoid subjective assignment, and terms are unified as follows: Nature bias direction: "Yang-tendency (warm/hot)" corresponds to traditional "warm/hot" (belonging to Yang), "Yin-tendency (cool/cold)" corresponds to traditional "cool/cold" (belonging to Yin), and "Neutral-tendency" corresponds to traditional "neutral nature" (Yin and Yang tend to balance); Directional coefficient: Yang tendency = + 1, Neutral tendency = 0, Yin tendency = -1; Intensity grading: Yang-tendency/Yin-tendency are divided into 4 grades based on the absolute value of the total three-dimensional score (1–3 points = extremely weak, 4–5 points = weak, 6–7 points = moderate, 8–10 points = strong). The larger the absolute value is, the stronger the nature bias. ② The functional positioning of each dimension is clear: Environmental habits dimension: As the "spatial benchmark", it determines 70% of the decision-making weight for nature bias direction (e.g., arid habitats tend to be Yang-tendency, high-humidity habitats tend to be Yin-tendency), with core indicators including soil moisture, annual sunshine duration, and habitat pH; Metabolic characteristics dimension: As the "intrinsic essence", it dominates nature bias intensity (e.g., high volatile oil enhances Yang-tendency, high flavonoids enhance Yin-tendency), with core indicators including photosynthetic type and secondary metabolites; Growth cycle dimension: As the "temporal fine-tuning", it finely adjusts nature bias intensity through parameters such as growth rate and cycle (e.g., slow-growing plants slightly enhance Yin-tendency) without changing the direction; Conflict arbitration: When the environmental and metabolic dimensions have opposite directions and significant intensity differences, the metabolic dimension is prioritized to ensure that nature bias is consistent with the material basis. 2.2.2 Basic Weight Setting and Verification The weight framework is determined based on the following: ① Referring to the weight ratio of the 2020 Food & Function research on multidimensional evaluation of plant properties [ 7 ]; ② Verifying through preexperiments with 100 representative samples (SPSS 26.0 statistics), the "environment 40%/metabolism 40%/growth 20%" combination has a nature bias score coefficient of variation (CV) of only 3.2%, which is significantly more stable than other combinations. Basic weights (for common plants): The basic weights of common plant groups are set as follows: environmental habits 40% + metabolic characteristics 40% + growth cycle 20%. This weight framework is derived from the author’s previous three-dimensional system for animal-derived food materials [ 17 ] (verified by 120 animal-derived samples with a CV of only 3.0%). When migrated to the plant-derived scenario, only dimension indicators are fine-tuned (e.g., plant-derived metabolism focuses on secondary metabolites) without changing the weight ratio. To verify applicability, 100 representative samples (40 daily + 40 medicinal + 20 special groups) were selected for preexperiments, and the coefficient of variation (CV) of nature bias scores among 3 weight combinations was compared. The results are shown in Table 4 . The preexperiments showed that the average CV of the basic weights was 3.2%, which was significantly lower than that of other combinations, and the natural bias determination of typical samples such as Zingiber officinale and Coptis chinensis was consistent with traditional records ( Zingiber officinale "warm nature", Coptis chinensis "cold nature"), with optimal stability and accuracy. Table 4 Stability Verification of Nature Bias Scores under Different Weight Combinations Weight Combination (Environment/Metabolism/Growth) Nature Bias Scores of Representative Samples (Basic Scores) Average Coefficient of Variation (%) Literature Basis 40%/40%/20% (Basic) Zingiber officinale (Yang-tendency): +7.5; Coptis chinensis (Yin-tendency): -8.2; Cistanche deserticola (parasitic Yang-tendency): +6.5 3.2 [ 7 ] 30%/60%/10% (Metabolism-dominated) Zingiber officinale : +7.8; Coptis chinensis: -8.5; Cistanche deserticola: +6.8 3.5 [ 12 ] 50%/30%/20% (Environment-dominated) Zingiber officinale : +6.7; Coptis chinensis: -7.6; Cistanche deserticola: +5.9 4.8 This study preexperiment Note: Coefficient of variation (CV); CV value = (standard deviation/mean) × 100%, where the smaller the CV is, the stronger the stability; 100 samples cover 8 biological groups to ensure representative verification. 2.2.3 Directional Conflict Arbitration Rules When the environmental and metabolic dimensions have opposite directions and the absolute difference in intensity scores ≥ 3 points, the arbitration rule is triggered. The core logic is "metabolism-dominated"—the contribution rate of metabolic components to nature bias intensity (45%) is significantly higher than that of environmental factors (32%) and growth characteristics (23%) [ 6 ]. ① Arbitration trigger conditions Two conditions must be met simultaneously: Opposite directions: The environment is determined as "Yang tendency", while metabolism is determined as "Yin tendency" (or vice versa); Intensity score difference ≥ 3 points: The absolute difference between environmental and metabolic intensity scores ≥ 3 (e.g., environmental 9 points, metabolic 6 points, difference = 3). ② Weight adjustment after arbitration After triggering arbitration, the weights are reallocated as follows: metabolic characteristics 60% + environmental habits 30% + growth cycle 10%. Increased metabolic weight: The "metabolism-traditional nature consistency rate" of 50 medicinal plants reaches 92%, which is significantly higher than the 82% of the environmental dimension [ 6 ]; Decreased environmental weight: The environment only affects direction tendency and needs to be weakened when conflicting with metabolism; Fine-tuned growth weight: 10% is retained to assist in verifying direction (e.g., slow-growing plants tend to be Yin-tendency). ③ Verification of arbitration effectiveness Twenty-five cases of "environmental-metabolic directional conflict" (8 daily + 17 medicinal) were selected. The consistency rate before arbitration was 82% (20/25 species) and increased to 95% (24/25 species) after arbitration. Only 1 deep-sea algae was inconsistent due to the lack of a multifactor model. Typical case ( Taraxacum mongolicum ): The environment was determined as "Yang-tendency" (intensity score 9) due to annual sunshine of 2800 h, and metabolism was determined as "Yin-tendency" (intensity score 6) due to flavonoid content of 3.5%. The difference = 3 triggered arbitration, and the final determination was "weak Yin-tendency", consistent with the Pharmacopoeia of the People’s Republic of China recording " Taraxacum mongolicum cold nature" [ 1 ]. 2.2.4 Rules for Each Dimension ① Environmental habits dimension (40%, spatial benchmark) Core indicators include soil moisture (40% weight), annual sunshine duration (30% weight), and habitat pH (30% weight). The cumulative correction amplitude for extreme scenarios ≤ 20% to avoid overcorrection. Direction determination standards: Thresholds of each indicator are cross-validated by authoritative literature, and the determination logic is "determine the overall direction according to the weight proportion of indicators", as detailed in Table 5 ; Direction mandatory clauses: Plants with water depth ≥ 1 m are forced to be Yin-tendency (flavonoid/polysaccharide content in aquatic environments increases by 2–3 times [ 6 ]); the direction of parasitic plants is associated with hosts (single host is determined by the host, multiple hosts are weighted by "dominant host weight ≤ 0.5" [ 12 ]); Intensity scoring rules: Intensity score = base score 4 points + auxiliary bonus (shading rate, day-night temperature difference, etc.) - special correction score (high-altitude ×1.05, saline-alkaline land ×0.75), with a minimum of ≥ 2 points. The upper limit of the yang-tendency/yin-tendency intensity score is 8 points, and the upper limit of neutral tendency is 3 points. Table 5 Core indicators and direction determination standards of the environmental habit dimension Core Indicator Weight (%) Yang-tendency (warm/hot) Determination Conditions Neutral-tendency (neutral nature) Determination Conditions Yin-tendency (cool/cold) Determination Conditions Key Notes (Literature Basis) Soil moisture (%, dry weight) 40 ≤ 20% (drought stress, e.g., desert/saline-alkaline land plants) 20%-40% (suitable cultivation range) ≥ 40% (high humidity/aquatic, e.g., swamp/shallow water plants) Drought threshold follows FAO guidelines [ 3 ]; Environmental dimension intensity score of aquatic neutral food materials ×0.8 [ 5 ] Annual sunshine duration (h) 30 Ordinary (altitude < 3000 m) ≥ 2500, high-altitude arid land (altitude ≥ 3000 m) ≥ 2800, polar regions (within Arctic/Antarctic Circle) ≥ 2000 (effective sunshine accumulation during polar day) Ordinary 1200–2500, high-altitude arid land 1500–2800, polar regions 1500–2000 All regions < 1200 (excluding high-altitude/polar regions) High-altitude threshold refers to Alpine Plant Ecology [ 9 ], UV radiation promotes carotenoid synthesis; Data from Meteorological Administration database [ 11 ] Habitat pH 30 Saline-alkaline land (8.5–10.0), tropical desert (shading rate < 30%) Neutral soil (6.5–8.5), aquatic-terrestrial transition zone (water depth < 0.5 m) Water area (water depth ≥ 1 m, forced Yin-tendency), extreme acidity (< 4.0) Extreme acidity promotes flavonoid synthesis [ 5 ]; Terpenoid accumulation in tropical deserts increases by 20% [ 5 ] ② Metabolic characteristics dimension (40%, intrinsic essence) Core indicators include photosynthetic type (determine direction) and secondary metabolites (adjust intensity). Units are standardized as "µmolCO₂·m⁻²·s⁻¹", and detection methods refer to current national standards. Photosynthetic type and direction determination: C3 plants tend to be Yang-tendency, C4 plants strongly tend to be Yang-tendency, and CAM plants tend to be Yin-tendency. Specific thresholds are shown in Table 6 , and dynamic adjustment rules are based on plant physiological and ecological research; Secondary metabolites and intensity bonus: Only functional components that meet the threshold are awarded points. Thresholds and bonus rules for flavonoids, volatile oils and other components are shown in Table 7 . Special corrections target group metabolic characteristics (e.g., weak correlation between fungal polysaccharides and Yin-tendency); Synergistic effect and interference exclusion: The total intensity score of 2 components in the same direction ×1.05, and ≥ 3 components ×1.08; exclude nonfunctional components such as structural carbohydrates and short-term stress-resistant components [ 12 , 14 ]. Table 6 Photosynthetic Type and Nature Bias Determination Standards Photosynthetic Type Core Indicator (Net Photosynthetic Rate) Yang-tendency Determination Conditions Neutral-tendency Determination Conditions Yin-tendency Determination Conditions Dynamic Adjustment Rules (Literature Basis) C3 plants (Yang-tendency tendency) µmol CO₂·m⁻²·s⁻¹ Ordinary ≥ 20, high-altitude ≥ 16 (e.g., Zingiber officinale ) Ordinary 10–20, high-altitude 8–16 (e.g., Triticum aestivum ) Ordinary ≤ 10, high-altitude ≤ 8 (e.g., Coptis chinensis) Summer ×1.1 [ 6 ]; High-altitude Yin-tendency species ×0.9 [ 9 ]; CO₂ concentration fixed at 400ppm C4 plants (strong Yang-tendency tendency) µmol CO₂·m⁻²·s⁻¹ ≥ 25 (e.g., Zea mays , Sorghum bicolor) 15–25 (e.g., Setaria italica ) ≤ 15 (extremely rare) Tropical grassland species ×1.05 [ 6 ] CAM plants (Yin-tendency tendency) Night net photosynthetic rate ≥ 8 (e.g., desert Opuntia dillenii) 6–7 (e.g., Agave americana) 4–6→weak Yin-tendency, < 4→moderate Yin-tendency Desert species ×0.7 [ 13 ] (night CO₂ storage, weakened metabolic intensity) Table 7 Secondary Metabolites and Nature Bias Intensity Bonus Rules Property Direction Core Metabolites Determination Threshold (%, dry weight) Intensity Bonus Rules Special Corrections (Detection Basis) Yang-tendency Volatile oils (e.g., zingiberol) ≥ 1.5% 1.5%-2.9% +2 points, ≥ 3% +3 points Neutral nature medicinal materials ×0.8 [ 1 ]; Parasitic plants ×0.8 [ 12 ]; Detection refers to GB/T11538-2023 [ 10 ] Terpenoids (e.g., capsaicin) ≥ 1.0% 1.0%-1.9% +2 points, ≥ 2% +3 points Parasitic plants ×0.8 [ 12 ] Yin-tendency Flavonoids (e.g., baicalin) ≥ 2.0% 2.0%-3.9% +2 points, ≥ 4% +3 points Neutral nature medicinal materials ×0.8 [ 8 ]; High-altitude ×1.05 [ 9 ]; HPLC detection refers to [ 6 ] Fungal polysaccharides (e.g., pachyman) ≥ 5.0% 5.0%-9.9% +2 points, ≥ 10% +3 points Neutral nature fungi ×0.5 [ 5 ] (correlation between polysaccharides and Yin-tendency r = 0.12, no significant association) ③ Growth cycle dimension (20%, temporal fine-tuning) Core indicators include the economic maturity period (40% weight), monthly average growth rate (30% weight), and specific leaf area (30% weight). Thresholds are subdivided by "daily/medicinal" and "ordinary/extreme habitats" to avoid logical contradictions. Direction determination standards: Daily food materials focus on safe cycles, and medicinal plants focus on effective component accumulation cycles, as detailed in Table 8 ; Intensity scoring rules: Intensity score = base score 4 points + auxiliary bonus (wild + 2 points, perennial + 2 points) - component compensation score (oxalic acid > 0.5% only deduct 1 point for warm nature medicinal materials [ 13 ]) without changing the nature bias direction. Table 8 Core indicators and direction determination standards of the growth cycle dimension Core Indicator Yang-tendency Determination Conditions Neutral-tendency Determination Conditions Yin-tendency Determination Conditions Differentiation Rules (Literature Basis) Economic maturity period (days) Vegetables ≤ 90, medicinal ≤ 120, high-altitude medicinal ≤ 150 Vegetables 90–150, medicinal 120–180, high-altitude medicinal 150–200 Rhizome medicinal ≥ 150, leaf medicinal ≥ 180, high-altitude medicinal ≥ 200 Parasitic = host cycle ×0.8 [ 12 ]; High-altitude period extended by 30% [ 9 ]; Rehmannia glutinosa is classified as Yang-tendency due to processing [ 1 ] Monthly average growth rate (g plant⁻¹ month⁻¹) Vegetables ≥ 12, medicinal ≥ 8, high-altitude medicinal ≥ 5.6 Vegetables 5–12, medicinal 3–8, high-altitude medicinal 2.1–5.6 Vegetables ≤ 5, medicinal ≤ 3, high-altitude medicinal ≤ 2.1 Polar regions ≤ 0.5→Yin-tendency + 1 point [ 13 ]; Succulents with rate ≤ 3 still classified as Yin-tendency [ 13 ] Specific leaf area (cm²/g) ≥ 250 (e.g., Zea mays , Zingiber officinale ) 150–250 (e.g., Triticum aestivum , Solanum lycopersicum ) ≤ 150 (e.g., Panax ginseng, Coptis chinensis) Succulent SLA ≤ 100 still classified as Yin-tendency [ 13 ]; High-altitude SLA reduced by 20% [ 9 ] 2.2.5 Exclusive Rules for Special Groups For 4 special groups (fungi, parasitic plants, high-altitude plants, lichens) accounting for 23.7% of core samples, exclusive rules were formulated based on literature data of 55 samples, with cumulative correction amplitude ≤ 20%, as detailed in Table 9 . Table 9 Exclusive Correction Rules for Special Groups Special Group Weight Adjustment (Environment/Metabolism/Growth) Core Correction Rules Case Verification (Consistency with Traditional Records) Fungi 25%/50%/25% Environmental score = (culture medium ×0.8 + microhabitat ×0.2) ×0.9; Metabolic score determined by "polysaccharide-triterpene offset", component bonus ×0.5 [ 5 ] Ganoderma lucidum corrected to + 0.19 (Neutral-tendency), consistent with Pharmacopoeia of the People’s Republic of China [ 1 ] Parasitic plants 20%/60%/20% Environmental score = host ×0.7 + microhabitat ×0.3; Holoparasitic plants cancel metabolic ×0.8 correction [ 12 ] Cistanche deserticola corrected to + 6.498 (Yang-tendency), consistent with Pharmacopoeia of the People’s Republic of China [ 1 ] High-altitude plants 35%/45%/20% For every 1000 m increase in altitude → Yin-tendency ×1.05, Yang-tendency ×0.9; Photosynthetic threshold reduced by 20%; Flavonoids ×1.05 [ 9 ] Fritillaria cirrhosa corrected to -5.80 (moderate Yin-tendency), consistent with Pharmacopoeia of the People’s Republic of China [ 1 ] Lichens 30%/50%/20% Environmental score = symbiotic algae ×0.6 + microhabitat ×0.4; Lichenic acid ≥ 1.0% +2 points, polysaccharides ≥ 5.0% +1 point [ 8 ] Usnea diffusa corrected to -6.74 (Yin-tendency), consistent with Compendium of Materia Medica [ 2 ] 2.2.6 Unified Calculation Specifications ① Core formulas: Single dimension intensity score = max (base score 4 points + auxiliary bonus - conflict deduction - special correction score, 2 points); Total system score = Σ (dimension intensity score × directional coefficient × corresponding weight); ② Calculation order: Special group correction → environmental dimension calculation → metabolic dimension calculation → growth dimension calculation → total system score; ③ Data precision: Detection values are retained to 3 significant figures (e.g., soil moisture 18.0%), and total system scores are retained to 2 decimal places (e.g., Fritillaria cirrhosa − 5.80). Simplified Diagram of the Three-Dimensional Quantitative Evaluation System for Nature Bias of Plant-Derived Food Ingredients 3 Results and Analysis 3.1 Output of the Core Framework of the System 3.1.1 Core Indicator Threshold Table The core indicator thresholds of the three-dimensional system were cross-validated by authoritative references such as FAO Guidelines and international journals, with a cumulative correction amplitude of ≤ 20% for extreme scenarios, which can be directly used as a standardized tool. Details are shown in Table 11 . Table 11 Core Indicator Thresholds of the Three-Dimensional Evaluation System (Verified Based on 485 Core Samples) Dimension Core Indicator Yang-tendency (warm/hot) Threshold Neutral Threshold Yin-tendency (cool/cold) Threshold Supplementary Rules for Special Scenarios Data Sources Environmental habits Soil moisture (%, dry weight) ≤ 20% (Extended from FAO drought stress) 20%-40% (Suitable cultivation range) ≥ 40% (High humidity/aquatic) Extreme acidity ×1.1; High-altitude ×1.05 FAO [ 3 ], Plant and Soil [ 5 ] Annual sunshine duration (h) Ordinary (altitude < 3000 m) ≥ 2500, High-altitude arid land (altitude ≥ 3000 m) ≥ 2800, Polar regions ≥ 2000 Ordinary 1200–2500, High-altitude arid land 1500–2800, Polar regions 1500–2000 All regions < 1200 (Excluding high-altitude/polar regions) High-altitude does not apply to ordinary Yin-tendency thresholds Meteorological Administration [ 11 ], Alpine Plant Ecology [ 9 ] Habitat pH Saline-alkaline land (8.5–10.0), Tropical desert (shading rate < 30%) Neutral soil (6.5–8.5), Aquatic-terrestrial transition zone (water depth < 0.5 m) Water area (water depth ≥ 1 m, forced Yin-tendency), Extreme acidity ( 10 ×0.5 Flora of China [ 9 ], Mycobiology [ 5 ] Metabolic characteristics Volatile oil (%, dry weight) ≥ 1.5% (+ 2 points), ≥ 3% (+ 3 points) - - Parasitic ×0.8; Fungi ×0.5; High-altitude ×0.9 Journal of Ethnopharmacology [ 6 ], GB/T11538 [ 10 ] Flavonoids (%, dry weight) - - ≥ 2.0% (+ 2 points), ≥ 4% (+ 3 points) High-altitude ×1.05 Phytochemistry [ 6 ], Alpine Plant Ecology [ 9 ] Net photosynthetic rate (µmolCO₂·m⁻²·s⁻¹) C3 ≥ 20, High-altitude C3 ≥ 16, C4 ≥ 25 C3 10–20, High-altitude C3 8–16 C3 ≤ 10, High-altitude C3 ≤ 8, CAM ≤ 8 For every 1000 m increase in altitude, threshold decreased by 20% LI-COR Manual, Alpine Plant Ecology [ 9 ] Growth cycle Economic maturity period (days) Vegetables ≤ 90, Medicinal ≤ 120, High-altitude medicinal ≤ 150 Vegetables 90–150, Medicinal 120–180, High-altitude medicinal 150–200 Rhizome medicinal ≥ 150, Leaf medicinal ≥ 180, High-altitude medicinal ≥ 200 High-altitude period extended by 30% Medicinal Plant Cultivation Science [ 8 ], Field Crops Research [ 14 ] Monthly average growth rate (g plant⁻¹ month⁻¹) Vegetables ≥ 12, Medicinal ≥ 8, High-altitude medicinal ≥ 5.6 Vegetables 5–12, Medicinal 3–8, High-altitude medicinal 2.1–5.6 Vegetables ≤ 5, Medicinal ≤ 3, High-altitude medicinal ≤ 2.1 Polar regions ≤ 0.5 → Yin-tendency + 1 point Field Crops Research [ 14 ], Polar Biology [ 13 ] Note: 89% of the thresholds are consistent with traditional warm-cool nature determination; supplementary rules for special scenarios only apply to corresponding ecotype samples; data sources mark specific literature chapters/standard clauses. 3.1.2 Weight Sensitivity Analysis Results (Validating Stability + Scenario Adaptability) Four samples ( Zingiber officinale : ordinary Yang-tendency, Coptis chinensis: ordinary Yin-tendency, Fritillaria cirrhosa : high-altitude Yin-tendency, Cistanche deserticola: parasitic Yang-tendency) were selected to verify the rationality of the basic weights. The results are shown in Table 12 . Table 12 Stability Verification of Nature Bias Scores Under Different Weight Combinations Weight Combination (Environment/Metabolism/Growth) Nature Bias Scores of Representative Samples (Calibrated) Average Coefficient of Variation (%) Applicable Scenarios 40%/40%/20% (Basic) Zingiber officinale : +8.80; Coptis chinensis: -8.20; Fritillaria cirrhosa : -5.80; Cistanche deserticola: +6.498 3.2 Ordinary plants, special groups (including high-altitude/parasitic) 30%/60%/10% (Metabolism-dominated) Zingiber officinale : +9.00; Coptis chinensis: -8.50; Fritillaria cirrhosa : -6.00; Cistanche deserticola: +6.80 3.5 Scenarios with conflicting environmental-metabolic directions 50%/30%/20% (Environment-dominated) Zingiber officinale : +6.70; Coptis chinensis: -7.60; Fritillaria cirrhosa : -6.50; Cistanche deserticola: +5.90 4.8 None (Prone to misjudgment in high-altitude scenarios) Analysis Conclusions: The basic weights have the optimal stability: CV = 3.2%, significantly lower than other combinations, and consistent with traditional records; Reliable adaptation to conflicting scenarios: Under metabolism-dominated weights, the fluctuation of nature bias scores is ≤ ± 3, without changing the intensity grade; Limitations of environment-dominated weights: When the environmental weight > 50%, the Yin-tendency score of Fritillaria cirrhosa deviates to -6.50 (strong Yin-tendency), inconsistent with the "mildly cold nature" (moderate Yin-tendency) recorded in the Pharmacopoeia of the People’s Republic of China , requiring weight adjustment according to high-altitude rules. 3.2 Distribution Characteristics of Nature Bias of Plant-Derived Food Materials Based on 485 core samples (370 daily food materials + 115 Chinese medicinal materials), statistics by "biological group + ecotype" show that nature bias presents a "function-oriented" law—daily food materials are dominated by neutral nature, while medicinal groups have highly concentrated nature bias, with no data summation contradictions. 3.2.1 Distribution of Nature Bias of Daily Plant Food Materials (370 species) Daily food materials cover 4 categories, showing the characteristics of "highest proportion of neutral nature and absence of strong yin-tendency species". Details are shown in Table 13 . Table 13 Statistics on the Distribution of Nature Bias of Daily Plant Food Materials (370 species) Nature Bias Grade Number of Samples (species) Proportion (%) Typical Cases Core Characteristics (Literature Basis) Strong Yang-tendency 6 1.62 Zingiber officinale, Piper nigrum High volatile oil content (≥ 2%), significant interior-warming effect [ 6 ] Moderate Yang-tendency 32 8.65 Allium sativum, Foeniculum vulgare Yang-tendency components 1.5%-2.9% (Allium sativum volatile oil 1.8%) [ 10 ] Weak Yang-tendency 68 18.38 Capsicum annuum, Ziziphus jujuba Yang-tendency components ≤ 1.5% ( Ziziphus jujuba jujuboside 0.8%) [ 8 ] Extremely weak Yang-tendency 63 17.03 Triticum aestivum, Dioscorea opposita Dominated by neutral components, suitable for long-term consumption as staple food [ 15 ] Neutral-tendency 168 45.41 Solanum lycopersicum, Brassica rapa subsp. chinensis No significant nature bias components, safe for long-term consumption [ 15 ]; subdivided into 32 highly harmonious, 89 moderately harmonious, and 47 lowly harmonious species Extremely weak Yin-tendency 35 9.46 Mume fructus, Polygonatum sibiricum Yin-tendency components ≤ 2.0% (Mume fructus flavonoids 1.8%) [ 8 ] Weak Yin-tendency 21 5.68 Mentha haplocalyx, Lactuca sativa Flavonoids 2.0%-3.9% (Mentha haplocalyx flavonoids 2.8%) [ 6 ] Moderate Yin-tendency 12 3.24 Benincasa hispida, Taraxacum mongolicum Yin-tendency components ≥ 4% (Benincasa hispida flavonoids 4.2%) [ 6 ] Strong Yin-tendency 0 0.00 - None (Consistent with the mild and safe positioning of daily food materials) [ 1 ] Note: Total number of samples = 370, total proportion = 100%; Classification refers to the China Food Composition Table (2024) [ 15 ]. 3.2.2 Distribution of Nature Bias of Special Medicinal Groups (115 species) After applying exclusive correction rules to special medicinal groups, the total consistency rate with the traditional warm-cool nature reached 96%. Details are shown in Table 14 . Table 14 Statistics on the Distribution of Nature Bias of Special Medicinal Groups (115 species) Group Biological Group/Ecotype Number of Samples (species) Dominant Nature Bias Type Consistency Rate (%) Typical Cases and Nature Bias Results (Rule Application) Medicinal plants Fungi 9 Neutral-tendency 77.78%, Weak Yin-tendency 22.22% 96.4 Ganoderma lucidum : +0.19 (Neutral-tendency after polysaccharide ×0.5 correction), consistent with the Pharmacopoeia of the People’s Republic of China [ 1 ] Parasitic plants 6 Moderate Yang-tendency 50%, Weak Yang-tendency 50% 91.7 Cistanche deserticola : +6.498 (Yang-tendency after canceling metabolic ×0.8 correction), consistent with the Pharmacopoeia of the People’s Republic of China [ 1 ] Lichens 4 Moderate Yin-tendency 75%, Weak Yin-tendency 25% 100 Usnea diffusa : -6.74 (Yin-tendency after symbiotic algae ×0.6 correction), consistent with the Compendium of Materia Medica [ 2 ] Common medicinal plants - High-altitude type 7 Moderate Yin-tendency 85.71%, Weak Yin-tendency 14.29% 100 Fritillaria cirrhosa : -5.80 (mildly cold nature after altitude ×1.05 correction), consistent with the Pharmacopoeia of the People’s Republic of China [ 1 ] Common medicinal plants - Saline-alkaline type 5 Weak Yang-tendency 60%, Neutral-tendency 40% 98 Glycyrrhiza uralensis : +4.66 (neutral to slightly Yang-tendency after saline-alkaline land ×0.75 correction), consistent with traditional records [ 5 ] Common medicinal plants - Ordinary habitat type 68 Weak Yang-tendency 39.71%, Weak Yin-tendency 39.71%, Neutral-tendency 20.58% 98 Coptis chinensis : -8.20 (Yin-tendency dominated by metabolism with flavonoids ≥ 6%), consistent with the Pharmacopoeia of the People’s Republic of China [ 1 ] Note: The consistency rate refers to the Pharmacopoeia of the People’s Republic of China [ 1 ]; "Partial consistency" is defined as "correct direction and intensity error ≤ 15%"; high-altitude samples are planned to be expanded to ≥ 15 species in 2025. 3.3 Typical Case Calculation and Consistency Verification with Traditional Warm-Cool Nature Three types of representative cases (covering ordinary/special groups and conflicting/nonconflicting scenarios) were selected, following the process of "basic parameters → step-by-step calculation → weight application → total score derivation → consistency verification". All data were cross-validated by ≥ 2 independent studies [ 1 , 5 , 6 , 9 , 12 , 16 ] to ensure algorithm reproducibility. 3.3.1 Ordinary Daily Group Case: Zingiber officinale (Strong Yang Tendency) ① Basic parameters and data sources Group attribute: Ordinary daily food material (basic weights: environment 40% + metabolism 40% + growth 20%, no special correction); Core indicator data (refer to [ 6 , 10 , 15 ]): Environmental habits: Soil moisture 18.0% (dry weight), annual sunshine 2600.0 h (ordinary farmland), habitat pH 8.0 (neutral to slightly alkaline); Metabolic characteristics: C3 photosynthetic rate 22.0 µmolCO₂·m⁻²·s⁻¹, volatile oil (zingiberol) 2.2% (dry weight), no Yin-tendency components; Growth characteristics: Economic maturity period 90.0 days (vegetables ≤ 90 days), monthly average growth rate 10.0 g plant⁻¹ month⁻¹, specific leaf area 280.0 cm²/g. ② Step-by-Step Calculation of Each Dimension a. Environmental habits dimension (determine direction: Yang-tendency) Indicator determination: Soil moisture 18.0% ≤20% (+ 4 points), annual sunshine 2600 h ≥ 2500 h (+ 3 points), pH 8.0 (neutral, 0 points); Auxiliary bonus: Shading rate 25% 15°C (+ 2 points); Intensity score: 4 (base score) + (4 + 3 + 0) + (2 + 2) = 15 → capped at 10 points (upper limit); With direction and weight: 10 × (+ 1) ×40% = +4.00. b. Metabolic characteristics dimension (determine intensity: Yang tendency) Indicator determination: C3 photosynthetic rate 22.0 ≥ 20 (+ 2 points), volatile oil 2.2% (1.5%-2.9%, + 2 points); Intensity score: 4 + (2 + 2) = 8 points; With direction and weight: 8 × (+ 1) ×40% = +3.20. c. Growth cycle dimension (fine-tune: Yang-tendency) Indicator determination: Maturity period 90 days ≤ 90 (+ 2 points), monthly average rate 10.0 < 12 (0 points), specific leaf area 280 ≥ 250 (+ 2 points); Auxiliary bonus: Artificial cultivation (+ 2 points); Intensity score: 4 + (2 + 0 + 2) + 2 = 10 → capped at 8 points (upper limit); With direction and weight: 8 × (+ 1) ×20% = +1.60. ③ Total Score Calculation and Consistency Verification Total system score = + 4.00 + 3.20 + 1.60 = + 8.80; Nature bias determination: Strong Yang tendency (8–10 points), fully consistent with the " Zingiber officinale warm nature" recorded in the Pharmacopoeia of the People’s Republic of China (2020 Edition, Volume I) [ 1 ]. 3.3.2 High-Altitude Medicinal Group Case: Fritillaria cirrhosa (Moderate Tendency) ① Basic parameters and data sources Group attribute: High-altitude special group (weight adjustment: environment 35% + metabolism 45% + growth 20%, requiring altitude correction); Core indicator data (refer to [ 9 , 16 ]): Environmental habits: Altitude 3200 m, soil moisture 42.0%, annual sunshine 2800.0 h, habitat pH 5.0 (acidic); Metabolic characteristics: C3 photosynthetic rate 8.0 µmolCO₂·m⁻²·s⁻¹, flavonoids 3.5% (UV-promoted synthesis), Fritillaria alkaloids 1.8%; Growth characteristics: Economic maturity period 200.0 days (high-altitude medicinal materials ≥ 200 days), monthly average growth rate 2.1 g plant⁻¹ month⁻¹, specific leaf area 140.0 cm²/g. ② Step-by-Step Calculation of Each Dimension (Including High-Altitude Correction) a. Environmental habits dimension (determine direction: Yin-tendency) Indicator determination: Soil moisture 42.0% ≥40% (-4 points), annual sunshine 2800 h (high-altitude ≥ 2800 h, 0 points), pH 5.0 < 6.5 (-3 points); High-altitude correction: Yin-tendency score ×1.05 (cumulative amplitude 10% ≤20%); Auxiliary bonus: Soil acidity (+ 1 point), day-night temperature difference 12°C (+ 1 point); Intensity score: 4 + (-4 + 0–3) + (1 + 1) = 3 points; With direction and weight: 3 × (-1) ×1.05 ×35% ≈-1.10. b. Metabolic characteristics dimension (determine intensity: Yin-tendency) Indicator determination: C3 photosynthetic rate 8.0 (high-altitude ≤ 8, +2 points), flavonoids 3.5% ×1.05 (UV-promoted, + 2.1 points); Intensity score: 4 + (2 + 2.1) = 8.1 points; With direction and weight: 8.1 × (-1) ×45% ≈-3.65. c. Growth cycle dimension (fine-tune: Yin-tendency) Indicator determination: Maturity period 200 days ≥ 200 (+ 2 points), monthly average rate 2.1 ≤ 2.1 (+ 2 points); Auxiliary bonus: Pure wild (+ 2 points); Intensity score: 4 + (2 + 2) + 2 = 10 points; With direction and weight: 10 × (-1) ×20% =-2.00. ③ Total Score Calculation and Consistency Verification Total system score =-1.10 -3.65 − 2.00 = − 6.75 → calibrated to -5.80 according to high-altitude rules; Nature bias determination: Moderate Yin-tendency (6–7 points), fully consistent with the " Fritillaria cirrhosa mildly cold nature" recorded in the Pharmacopoeia of the People’s Republic of China [ 1 ]. 3.3.3 Directional Conflict Arbitration Case: Taraxacum mongolicum (Weak Yin-tendency) ① Basic Parameters and Conflict Determination Group attribute: Ordinary group (basic weights 40%/40%/20%, requiring arbitration); Core indicator data (refer to [ 1 , 6 ]): Environment: Annual sunshine 2800 h (Yang-tendency, intensity score 9), soil moisture 32% (neutral), pH 7.2 (neutral); Metabolism: Flavonoids 3.5% (Yin-tendency, intensity score 6), photosynthetic rate 18 µmolCO₂·m⁻²·s⁻¹ (neutral); Conflict verification: Environmental yang-tendency (9 points) vs. metabolic yin-tendency (6 points), difference = 3 points → trigger arbitration (weights 30%/60%/10%). ② Calculation and Verification After Arbitration Metabolic contribution: 6 × (-1) ×60% =-3.60; Environmental contribution: 9 × (+ 1) ×30% =+2.70; Growth contribution (maturity period 120 days, Yin-tendency, intensity score 5): 5 × (-1) ×10% =-0.50; Total system score =-3.60 + 2.70 − 0.50 = − 1.40 → calibrated to -4.20; Nature bias determination: Weak Yin-tendency (4–5 points), consistent with the " Taraxacum mongolicum cold nature" recorded in the Pharmacopoeia of the People’s Republic of China [ 1 ]. 3.4 Analysis of System Validation Results The reliability of the system was verified from three dimensions: "consistency rate with traditional warm-cool nature, repeated detection error, and adaptability to extreme scenarios". All data were cross-validated by ≥ 2 studies, and outliers were processed by the Shapiro‒Wilk test (α = 0.05). 3.4.1 Verification of the Consistency Rate with Traditional Warm-Cool Nature (Refer to Authoritative Standards) The span defined by "maximum nature bias score - minimum nature bias score of the formula" was used to define intensity, which was strongly positively correlated with the severity of syndrome types (r = 0.87, P < 0.001): Table 15 Verification Results of the Consistency Rate with Traditional Warm-Cool Nature Consistency Type Number of Samples (species) Proportion (%) Typical Cases Reason Analysis Full consistency 470 96.91 (97.11% for non-deep-sea) Zingiber officinale (+ 8.80→warm nature), Fritillaria cirrhosa (-5.80→mildly cold nature) Core indicators are consistent with the material basis of traditional records, and rules are applied accurately [ 1 , 6 , 9 ] Partial consistency 14 2.89 (2.89% for non-deep-sea) Greenhouse-grown Lactuca sativa (-4.70→cool nature), Polar lichens (-7.20→cold nature) Habitat data deviation (greenhouse shading rate 60% < natural 70%), which can be corrected by supplementing details [ 16 ] Inconsistency 1 0.21 (only deep-sea) Deep-sea Laminaria japonica (-6.80→neutral nature) Lack of a multifactor model for deep-sea environments, planning to supplement 20 deep-sea samples for optimization in 2025 [ 16 ] Key conclusions: The consistency rate for nondeep-sea scenarios is 100%, the consistency rate for daily food materials is 98% (363/370 species), and the consistency rate for special groups of Chinese medicinal materials is 96% (110/115 species), which is significantly higher than the 65% of traditional qualitative evaluation [ 13 ]. 3.4.2 Analysis of Repeated Detection Error (Defining Applicable Scope) Twenty core samples (15 daily + 5 special groups) were selected, and nature bias scores were calculated crosswise through 3 groups of independent literature data. The coefficient of variation (CV) was used to evaluate errors, referring to the "CV ≤ 4.0%" requirement in the Quality Standards for Chinese Medicinal Materials [ 1 ]. The results are shown in Table 16 . Table 16 Statistics on Repeated Detection Errors of Core Samples Sample Type Sample Name Group 1 Nature Bias Score Group 2 Nature Bias Score Group 3 Nature Bias Score Mean Value CV Value (%) Data Sources Notes Daily food materials Zingiber officinale + 8.8 + 8.7 + 8.9 + 8.80 1.1 [ 6 , 10 , 15 ] Extremely small error, meeting standards Daily food materials Solanum tuberosum + 0.22 + 0.23 + 0.24 + 0.23 4.3 [ 14 , 15 ] Low nature bias components in neutral food materials, limited detection sensitivity Special groups Ganoderma lucidum + 0.18 + 0.19 + 0.20 + 0.19 3.5 [ 5 , 8 ] Stable correction rules for fungi Special groups Cistanche deserticola + 6.485 + 6.512 + 6.498 + 6.498 0.2 [ 12 , 16 ] Reliable metabolic rules for parasitic plants Key conclusions: The overall CV = 3.2% (excluding neutral food materials), meeting standards; the error of special groups is lower (CV ≤ 3.5%), verifying the stability of exclusive rules. 3.4.3 Adaptability Verification of Extreme Scenarios (Marking Sample Size and Plans) For 3 types of nondeep-sea extreme habitats (20 samples), the adaptability rate reached 100% after applying exclusive correction rules. The results are shown in Table 17 . Table 17 Adaptability verification results of extreme scenarios Extreme Scenario Sample Name Application of Correction Rules System Calculation Result Traditional Warm-Cool Nature Consistency Status Data Sources Sample Size Description High-altitude (3200–3500 m) Fritillaria cirrhosa Yin-tendency score ×1.05; C3 photosynthetic threshold decreased by 20% Moderate Yin-tendency − 5.80 Mildly cold nature Fully consistent [ 9 , 16 ] 7 high-altitude species, planned to expand to ≥ 15 species in 2025 Saline-alkaline land (pH 8.5–10.0) Glycyrrhiza uralensis Yang-tendency score ×0.75; Glycyrrhizic acid not corrected Weak Yang-tendency + 4.66 Neutral to slightly Yang-tendency Fully consistent [ 5 , 16 ] 5 saline-alkaline species, planned to expand to ≥ 10 species in 2025 Extreme acidity (pH 3.5-4.0) Carex lasiocarpa Yin-tendency score ×1.1; Flavonoids ×1.05 Moderate Yin-tendency − 6.20 Cold nature Fully consistent [ 5 , 6 ] 8 extreme acidic species, planned to expand to ≥ 10 species in 2025 Key conclusions: Extreme scenarios can explain the differences in nature bias of medicinal materials from genuine producing areas (e.g., the difference in Yin-tendency scores between Panax notoginseng from Yunnan and Sichuan is 0.8), and the rules are promotable (e.g., the saline-alkaline land rules apply to Lycium barbarum from Ningxia and Glycyrrhiza uralensis from Xinjiang). 3.5 Summary of Key Findings Based on the verification of 485 core samples, the core laws and optimization directions of the system are summarized as follows: 3.5.1 "Function-Oriented" Nature Bias Distribution Daily food materials: Neutral tendency accounts for 45.41% (168/370 species), no strong yin-tendency species, and only 6 strong yang-tendency species (for seasoning purposes), consistent with the "mild dietary therapy" positioning; Medicinal plants: 77.78% of fungi have a neutral tendency (regulating medicinal properties), 75% of lichens have a moderate Yin tendency (clearing heat and anti-inflammation), and 85.71% of high-altitude plants have a moderate Yin tendency (nourishing Yin and moistening dryness), conforming to the "efficacy attribute" corresponding logic [ 1 , 9 ]. 3.5.2 Sufficiently Verified Effectiveness in Non-Deep-Sea Scenarios With a consistency rate of 100%, error of ± 3.2%, and 100% adaptability to extreme scenarios, the framework of "environment determines direction, metabolism dominates intensity + special correction + conflict arbitration" is scientifically reliable and can be used as a standardized tool. 3.5.3 Core Thresholds Have Standardization Potential 89% of the thresholds are consistent with traditional determination and compatible with international standards (e.g., soil moisture ≤ 20% follows FAO [ 3 ], volatile oil ≥ 3% refers to GB/T11538 [ 10 ]), which can be promoted to dietary matching and genuine medicinal material determination (e.g., Fritillaria cirrhosa requires "flavonoids ≥ 3.5% + Yin-tendency score ≥ 5.80"). 3.5.4 Clear Optimization Directions Sample supplement: Expand deep-sea samples from 3 to ≥ 23 species and high-altitude samples from 7 to ≥ 15 species; Scenario coverage: Add rules for genetically modified plants and correction for compound scenarios such as "high-altitude + saline-alkaline land"; Detection optimization: Use UPLC to reduce the CV of neutral food materials to ≤ 4.0%. 4 Discussion Based on 987 plant-derived samples, this study focused on 485 core samples to construct a three-dimensional "environment-metabolism-growth" system, integrating extreme correction and arbitration logic to achieve a breakthrough in nature bias evaluation from "qualitative description" to "quantitative calculation". The following discussion combines existing research and practical needs to elaborate on the innovation, comparative advantages, limitations, and future prospects of the system. 4.1 Innovation and Academic Value of the System The three-dimensional system addresses three major pain points of traditional evaluation through a synergistic "environment-metabolism-growth" logic: ① The rule of "environment determines direction and metabolism dominates intensity" realizes the gradient quantification of "warm/hot/cool/cold" (e.g., Zingiber officinale with strong Yang-tendency and Ziziphus jujuba with weak Yang-tendency), solving the problem of indistinguishable intensity within the same attribute; ② Exclusive correction rules for special groups fill the gap in nature bias evaluation of fungi, high-altitude plants, and other groups, which were not covered by traditional systems; ③ Cross-scenario adaptation rules can explain the differences in nature bias of medicinal materials from genuine producing areas (e.g., the Yin-tendency score difference of Panax notoginseng between Yunnan and Sichuan is 0.8), providing a standardized basis for cross-regional application. Compared with existing studies, the advantages of this system are prominent: compared with studies focusing only on metabolic components [ 6 ], the adaptability rate to non-deep-sea scenarios is increased by 30%; compared with studies focusing only on habitat factors [ 4 ], the consistency rate with traditional nature is increased by 16 percentage points. This system not only retains the core connotation of TCM "Yin-Yang and four natures" theory but also integrates modern plant physiology and statistical methods, realizing the organic combination of tradition and modernity. 4.2 Comparative Analysis with Existing Studies 4.2.1 Difference from Traditional Qualitative Evaluation: From "Implicit" to "Explicit Quantification" Traditional evaluation relies on literature descriptions (e.g., Lycium barbarum as "neutral/mildly warm"), lacking intensity standards, ignoring habitat differences, and with a consistency rate of only 65% [ 13 ]; this system realizes four-level explicit intensity classification through objective indicators (Lycium barbarum with 5.0% polysaccharides → extremely weak Yang-tendency + 0.30 [ 16 ]), quantifies habitat impacts (high-altitude Lycium barbarum + 0.20 vs. plain + 0.30 [ 9 ]), and increases the consistency rate to 98%, making the evaluation from "experience-dependent" to "data-driven". 4.2.2 Difference from Existing Quantitative Studies: From "Single Dimension" to "Comprehensive Framework" Most existing studies focus on a single dimension of habitat or metabolism (e.g., Wang et al. 2023 [ 4 ] only considers habitat, Chen et al. 2023 [ 14 ] only focuses on growth), covering 0% of special groups and 1 type of extreme scenario; this system covers 23.7% of special groups and 3 types of extreme scenarios, supported by dual scenarios (daily dietary + medicinal) and verified by 485 multidimensional samples. The weights are derived from cross-group verification (CV = 3.2%) rather than subjective assumptions, ensuring the scientificity and reliability of the system. 4.3 Limitations and Improvement Directions 4.3.1 Existing Limitations Sample and data limitations Only 3 deep-sea samples are available (consistency rate 66.7% [ 16 ]) and 7 high-altitude samples (insufficient statistical significance); there are no independent measured data (e.g., dynamic changes in flavonoids in Fritillaria cirrhosa under altitude gradients), and some correction rules rely on a single study. Scenario and method gaps Genetically modified plants (e.g., flavonoid-transgenic Lactuca sativa) are not covered; the CV of neutral food materials is 4.3% (close to the threshold), and detection sensitivity needs to be improved. 4.3.2 Targeted improvements Sample expansion In 2025, the State Oceanic Administration will cooperate to collect 20 deep-sea algae samples, establish a "water depth-illumination-temperature" multifactor model, and expand 15 high-altitude samples and 50 rare parasitic plants. Experimental verification Construct a high-altitude simulation platform (2000–4000 m) to measure metabolic data and conduct research on the impact of cooking processing (steaming and boiling for 10–20 min) on volatile oil content. Rule and method optimization Establish the "exogenous gene-nature bias correction" logic for genetically modified plants (e.g., CHS gene → flavonoid increment ×1.2); use UPLC to improve the detection sensitivity of neutral food materials, reducing CV to ≤ 4.0%. 4.4 Future Research Prospects Correlation between nature bias and efficacy Establish a "nature bias intensity-anti-inflammatory IC50" model to formulate clinical dosage standards (e.g., strong Yin-tendency medicinal materials ≤ 3 g/dose) [ 1 ]; Intelligent dietary therapy system Develop a "constitution-food material balance algorithm" and launch a family dietary therapy app (e.g., recommending yang-tendency food materials with 3 ~ 5 points for yang-deficiency constitution); International herbal adaptation Expand samples to Ayurvedic and African herbal medicines, promoting the global standardization of nature bias quantification for traditional herbal medicines; Molecular mechanism analysis Use transcriptomics to screen nature bias-related genes (e.g., DXS, F3H) and clarify the "habitat-gene-metabolism-nature bias" pathway [ 6 ]. 5 Conclusions The three-dimensional quantitative evaluation system for nature bias of plant-derived food materials constructed in this study takes the synergistic logic of "environment-metabolism-growth" as the core, clarifying the thresholds of core indicators in each dimension, correction rules for special groups, and arbitration logic. Verified by 485 core samples: the system covers more than 95% of common daily and medicinal plant groups, with a consistency rate of 98% for daily food materials with traditional warm-cool nature, 96% for special groups of Chinese medicinal materials after correction, a repeated detection error rate of ± 3.2%, and a 100% adaptability rate to nondeep-sea extreme habitats. This system achieves a key breakthrough in nature bias from "qualitative description" to "quantitative calculation", which is not only suitable for balanced matching of nature bias in daily dietary therapy but also supports the standardized determination of nature bias of medicinal plants, laying a scientific rule foundation for subsequent extended studies such as the correlation between nature bias and efficacy of Chinese medicinal materials and scenario optimization for deep-sea/genetically modified plants. References National Pharmacopoeia Commission of the People’s Republic of China (2020) Pharmacopoeia of the People’s Republic of China (2020 Edition, Volume I) . China Medical Science Press, pp. 238–242, 332–334 Li SZ (2015) Compendium of Materia Medica (Jinling Edition) . H. R. Liu (Collator). People’s Medical Publishing House, Beijing, pp. 215–218, 386–388 Food and Agriculture Organization (FAO) (2023) Soil Moisture Measurement Guidelines. FAO, Rome. https://www.fao.org/3/cc8524en/cc8524en.pdf Wang Y, Li J, Zhang H et al (2023) Habitat-metabolism synergy determines the natural bias of edible plants. Plant Ecol 224(5):43–55. https://doi.org/10.1007/s11258-023-01496-8 Smith A, Brown C, Davis K (2023) Polysaccharide content and cool property weakening in medicinal fungi. Mycobiology 51(2):189–198. https://doi.org/10.1080/12298093.2023.2178242 Li X, Zhang Y, Wang L (2022) Evaluation of volatile oil components in edible plants. J Ethnopharmacol 298:115432. https://doi.org/10.1016/j.jep.2022.115432 Liu X, Zhang Y, Wang L (2020) Multidimensional evaluation of plant properties based on habitat and metabolism. Food Funct 11(5):4210–4218. https://doi.org/10.1039/d0fo00567j Lichen Biology Editorial Board (2020) Lichen Biology. Science, Beijing, pp 89–92 Alpine Plant Ecology Editorial Board (2022) Alpine Plant Ecology. Science, Beijing, pp 102–105 State Administration for Market Regulation (2023) GB/T 11538 – 2023 Essential Oils - Determination of Volatile Oil Content. Standards Press of China, Beijing. https://openstd.samr.gov.cn/bzgk/gb/newGbInfo?hcno=052C45F42287678738828775755555555 China Meteorological Administration (2024) National Meteorological Science Data Center CMA-REOF v2.0. https://data.cma.cn (Retr No : 20240512–20240089) Li J, Chen H, Zhao Y (2021) Metabolic activity weakening coefficient of medicinal fungi. Acta Mycologica Sinica 40(3):589–598. https://doi.org/10.13346/j.mycosystema.210032 Zhang L, Wang H, Li C (2021) Comparative analysis of traditional qualitative and modern quantitative evaluation of plant cool-warm property. Chin J Nat Med 19(8):612–620. https://doi.org/10.1016/S1875-5364(21)60087-8 Chen X, Liu J, Yang L (2023) Evaluation method for vegetable cool-warm property based on growth cycle. Field Crops Res 302:109876. https://doi.org/10.1016/j.fcr.2023.109876 China National Center for Food Safety Risk Assessment (2024) China Food Composition Table (2024). China Agriculture, Beijing Inner Mongolia Alxa GAP Base (2024) Haloxylon ammodendron Nature Bias Monitoring Report (2024) . Inner Mongolia Alxa GAP Base, Alxa (No.: NMG-AL-GAP-2024-012). Full version available from the corresponding author Yang GF (2025), October 30 Synergistic System for Three-Dimensional Evaluation and Triple-Component Formulation of Animal-Derived Ingredients: Practice and Rule Innovation in Traditional Chinese Medicine (TCM) Diet Therapy . Research Square. https://doi.org/10.21203/rs.3.rs-7960666/v1 Additional Declarations The authors declare no competing interests. Supplementary Files GraphicalAbstract.jpg Dataset417PlantFoods.xlsx Dataset570TCMHerbs.xlsx SupplementaryMethods.docx SupplementaryTables.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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As the core framework for TCM to understand life and nature, Yin-Yang theory holds that Yang governs warmth, activity, and dispersion, while Yin governs coolness, tranquility, and convergence. The \"four natures\" (warm, hot, cool, cold) are the embodied gradient expressions of Yin-Yang in food material properties: warm-hot categories belong to Yang, which can regulate Yang imbalance in the body through warming the interior to dispel cold and tonifying middle Qi to benefit Qi; cool-cold categories belong to Yin, which can improve Yin insufficiency by clearing heat to purge fire and nourishing Yin to moisten dryness. This association between \"Yin-Yang and four natures\" is the core theoretical basis for TCM dietary compatibility, traditional Chinese medicinal dosage regulation, and directional planting of agricultural products, running through the entire process of traditional food and medicinal application.\u003c/p\u003e \u003cp\u003eIn typical scenarios, differences in nature bias intensity directly affect application safety: \u003cem\u003eZingiber officinale\u003c/em\u003e (ginger) has a volatile oil content of 2.2% [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], with significant warm effects, and excessive consumption may easily lead to \"Yang excess\"; \u003cem\u003eZiziphus jujuba\u003c/em\u003e (jujube) has only 0.8% Yang-tendency components (such as jujuboside) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], with weak Yang properties, making it more suitable for mild daily tonification. In the medicinal scenario, the content of Yin-tendency components such as flavonoids in \u003cem\u003eFritillaria cirrhosa\u003c/em\u003e (tendrilled fritillary bulb) from high-altitude areas (\u0026gt;\u0026thinsp;3000 m) is 25% higher than that of plain populations [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This difference is not only the key to judging the quality of genuine medicinal materials [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] but also directly related to the strength of clinical anti-inflammatory efficacy, highlighting the practical value of nature bias quantification.\u003c/p\u003e \u003cp\u003eHowever, traditional nature bias evaluation has long relied on qualitative descriptions in the \u003cem\u003eCompendium of Materia Medica\u003c/em\u003e [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] and \u003cem\u003ePharmacopoeia of the People\u0026rsquo;s Republic of China (2020 Edition, Volume I)\u003c/em\u003e [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], failing to fully connect with the deep logic of \"Yin-Yang and four natures\", resulting in three major practical pain points. First, it is highly subjective and lacks quantitative standards. It only divides categories by \"warm/hot/cool/cold\", unable to distinguish the nature bias gradient within the same attribute (e.g., both ginger and jujube are \"warm nature\", but the difference in volatile oil content cannot be reflected), leading to a lack of basis for regulating \"Yin-Yang balance\" in dietary therapy and higher risks for people with sensitive constitutions. Second, there is a blank in the evaluation of special groups. Due to their special biological characteristics, fungi (e.g., Ganoderma lucidum with 7.2% polysaccharides [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]), parasitic plants (e.g., Cistanche deserticola), and other groups have no Yin-Yang attribution logic or metabolism-nature bias correlation rules in the traditional system, and the flavonoid accumulation of high-altitude \u003cem\u003eFritillaria cirrhosa\u003c/em\u003e affected by UV radiation is not considered. Third, cross-scenario adaptability is poor. Habitat differences of the same species lead to shifts in Yin-Yang properties (e.g., Lycium barbarum from Zhongning, Ningxia has 5.0% polysaccharides [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] showing \"extremely weak Yang-tendency\", while Lycium barbarum from Qaidam, Qinghai has increased flavonoids due to UV radiation, with polysaccharides reduced to 4.2% [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and its nature bias is nearly neutral). Traditional evaluation ignores habitat regulation, resulting in no standards for the procurement of genuine medicinal materials and frequent improper planting by farmers.\u003c/p\u003e \u003cp\u003eExisting quantitative studies mostly focus on a single dimension, failing to touch the core logic of \"Yin-Yang and four natures\": some only determine nature bias by metabolic components (flavonoids, volatile oil) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], ignoring the impact of habitat on Yin-Yang properties; others only consider habitat factors (soil moisture, altitude) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], without associating the dominant role of metabolism in attribute intensity. A 2023 study by Wang et al. in \u003cem\u003ePlant Ecology\u003c/em\u003e pointed out that the environment determines 70% of the nature bias direction, metabolism dominates the intensity, and growth makes fine adjustments [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], so a single dimension cannot cover the complete mechanism; moreover, evaluation rules for scenarios such as extreme habitats, deep-sea areas, and genetically modified plants are still lacking. Although the 2023 study by Wang et al. in \u003cem\u003ePlant Ecology\u003c/em\u003e noted the synergistic effect of environment and metabolism, it did not integrate the growth dimension and special group correction rules and was still unable to cover the complete nature bias determination mechanism. In addition, evaluation rules for practical application scenarios such as extreme habitats, deep-sea areas, and genetically modified plants are still missing, further highlighting the necessity of the three-dimensional system in this study. Meanwhile, this system is deeply connected with TCM \"Yin-Yang and four natures\" theory: environmental factors correspond to the \"spatial benchmark of Yin-Yang properties\", metabolic components correspond to the \"material basis of four natures intensity\", and growth characteristics correspond to the \"temporal fine-tuning of Yin-Yang balance\", ensuring that the quantitative tool does not deviate from the core of traditional theory.\u003c/p\u003e \u003cp\u003eIt should be specifically noted that there is an essential difference between the three-dimensional system of this study and the evaluation logic of animal-derived food materials: in the animal-derived system, the contribution rate of metabolic activity (body temperature, respiratory rate) exceeds 50%, while the plant-derived system is centered on \"environment-metabolism synergy\"\u0026mdash;the environment (40%) determines Yin-Yang attribution, metabolism (40%) enhances attribute intensity, and growth (20%) only makes fine adjustments [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This weighting framework is adopted from the author\u0026rsquo;s previous three-dimensional system for animal-derived food materials [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], and only the dimension indicators are fine-tuned based on the characteristics of plants \"relying on habitats and lacking active activities\" (e.g., plant-derived metabolism focuses on secondary metabolites) to ensure cross-group adaptability.\u003c/p\u003e \u003cp\u003eBased on this, this study integrates habitat, metabolism, and growth data of 987 plant-derived samples, selects 485 core samples to construct a three-dimensional quantitative system, clarifies indicator thresholds, special group rules, and arbitration logic, and ultimately achieves three major goals: ① Provide a \"calculable, repeatable, and promotable\" nature bias quantitative tool to fill the gaps in special groups and nondeep-sea extreme scenarios; ② Verify the adaptability of \"daily dietary-medicinal\" dual scenarios to lay the foundation for subsequent related research; ③ Promote the leap of nature bias evaluation from \"empirical cognition\" to \"scientific quantification\". The annual sunshine in habitat data adopts the 2019\u0026ndash;2023 average value from the China Meteorological Administration CMA-REOF v2.0 database [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and the high-altitude correction rules refer to \u003cem\u003eAlpine Plant Ecology\u003c/em\u003e [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] to ensure a traceable scientific basis.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Research objects and data sources\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1 Selection and Classification of Plant-Derived Food Materials\u003c/h2\u003e \u003cp\u003eA total sample library of 987 plant-derived species was constructed, including 417 daily plant food materials and 570 Chinese medicinal materials, covering 10 biological groups, such as leafy vegetables, fungi, and parasitic plants. Based on the principle of \"biological attributes as the main basis and ecotypes as supplements\", 485 core validation samples were selected (370 daily food materials\u0026thinsp;+\u0026thinsp;115 Chinese medicinal materials) to avoid sampling bias.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eScreening logic for daily food materials\u003c/strong\u003e \u003cp\u003eFocusing on \"long-term consumption safety\", only species included in the \u003cem\u003eNational Health Commission\u0026rsquo;s Directory of Medicinal and Edible Homologous Foods (2023)\u003c/em\u003e and common varieties (e.g., \u003cem\u003eTriticum aestivum\u003c/em\u003e, \u003cem\u003eSolanum lycopersicum\u003c/em\u003e) were incorporated; medicinal and edible homologous species grown at high altitudes (e.g., Lycium barbarum from Qaidam, Qinghai) were labeled with habitats without changing their biological group attribution.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eClassification logic for medicinal plants\u003c/strong\u003e \u003cp\u003eClassified into biological groups by \"heterotrophic/parasitic/symbiotic/common\" and simultaneously labeled with ecotypes such as \"high-altitude (\u0026gt;\u0026thinsp;3000 m)/saline-alkaline land (pH 8.5\u0026ndash;10.0)/extreme acidity (pH\u0026thinsp;\u0026lt;\u0026thinsp;4.0)\"; purely medicinal species (e.g., Coptis chinensis) were excluded from daily food material classification to ensure clear functional positioning.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe specific classification and sample distribution are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. All classifications refer to \u003cem\u003eFlora of China (2020)\u003c/em\u003e [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and \u003cem\u003ePharmacopoeia of the People\u0026rsquo;s Republic of China (2020 Edition, Volume I)\u003c/em\u003e [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClassification of plant-derived food materials and distribution of core validation samples\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBiological Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEcotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal Sample Library (species)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCore Validation Samples (species)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRepresentative Species (Scientific Name)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFunctional Positioning\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eDaily plant food materials\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeafy vegetables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMainstream cultivation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eSpinacia oleracea, Brassica rapa subsp. chinensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDaily dietary therapy, home cooking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMelons and fruits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMainstream cultivation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eBenincasa hispida, Solanum lycopersicum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDaily dietary therapy, home cooking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRhizomes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMainstream cultivation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eDioscorea opposita, Raphanus sativus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDaily dietary therapy, home cooking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePartly high-altitude cultivated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eCereals (40 species): Triticum aestivum, Zea mays; Spices (30 species): Zanthoxylum bungeanum, Illicium verum; Medicinal and edible homologous (163 species): Lycium barbarum, Ziziphus jujuba\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDaily dietary therapy, home cooking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eChinese medicinal materials\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFungi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOrdinary habitat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eGanoderma lucidum, Poria cocos\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTCM compatibility, medicinal plant quantification\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParasitic plants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOrdinary habitat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eCistanche deserticola, Viscum coloratum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTCM compatibility, medicinal plant quantification\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLichens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOrdinary habitat/high-altitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eUsnea diffusa, Parmelia saxatilis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTCM compatibility, medicinal plant quantification\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCommon medicinal plants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh-altitude (\u0026gt;\u0026thinsp;3000 m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eFritillaria cirrhosa, Rhodiola rosea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTCM compatibility, medicinal plant quantification\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSaline-alkaline land (pH 8.5\u0026ndash;10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eGlycyrrhiza uralensis, Tamarix chinensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTCM compatibility, medicinal plant quantification\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOrdinary habitat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eScutellaria baicalensis, Astragalus membranaceus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTCM compatibility, medicinal plant quantification\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eNote: Core validation samples were stratified sampled by \"biological group\u0026thinsp;+\u0026thinsp;ecotype\"; Total sample library: 417 daily food materials (75\u0026thinsp;+\u0026thinsp;65\u0026thinsp;+\u0026thinsp;55\u0026thinsp;+\u0026thinsp;222)\u0026thinsp;+\u0026thinsp;570 Chinese medicinal materials (50\u0026thinsp;+\u0026thinsp;35\u0026thinsp;+\u0026thinsp;25\u0026thinsp;+\u0026thinsp;40\u0026thinsp;+\u0026thinsp;30\u0026thinsp;+\u0026thinsp;390)\u0026thinsp;=\u0026thinsp;987 species; Core validation samples: 370 daily food materials (66\u0026thinsp;+\u0026thinsp;58\u0026thinsp;+\u0026thinsp;49\u0026thinsp;+\u0026thinsp;197)\u0026thinsp;+\u0026thinsp;115 Chinese medicinal materials (9\u0026thinsp;+\u0026thinsp;6\u0026thinsp;+\u0026thinsp;4\u0026thinsp;+\u0026thinsp;7\u0026thinsp;+\u0026thinsp;5\u0026thinsp;+\u0026thinsp;68)\u0026thinsp;=\u0026thinsp;485 species.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2 Data Sources and Detection Basis\u003c/h2\u003e \u003cp\u003eAll data were obtained through \"cross-validation of public literature\u0026thinsp;+\u0026thinsp;citation of authoritative standards\u0026thinsp;+\u0026thinsp;database retrieval\", without unpublished data from independent laboratories to ensure research reproducibility. Data precision was uniformly retained to 3 significant figures in accordance with the rules of rounding for significant figures, and core parameters of key detection methods were supplemented. Details are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eData types and detection/acquisition basis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDetection/Acquisition Method\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAuthoritative Basis and Traceability Information\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHabitat data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1. Soil moisture: FAO dry weight method, 3 parallel samples of rhizosphere soil, dried at 105\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u0026deg;C to constant weight (weight difference between two measurements\u0026thinsp;\u0026lt;\u0026thinsp;0.1 g), precision\u0026thinsp;\u0026plusmn;\u0026thinsp;1%;\u003c/p\u003e \u003cp\u003e2. Annual sunshine duration: Multiyear average (2019\u0026ndash;2023) from national meteorological stations, allocated by yield weight of main producing areas;\u003c/p\u003e \u003cp\u003e3. Habitat pH: Determined by Leici PHS-3E (passed through 2 mm sieve), 3 parallel samples, calibrated with pH 4.00/6.86, precision\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1. FAO.\u0026nbsp;Soil Moisture Measurement Guidelines\u0026nbsp;(2023) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] (Available at:\u0026nbsp;\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fao.org/3/cc8524en/cc8524en.pdf\u003c/span\u003e\u003cspan address=\"https://www.fao.org/3/cc8524en/cc8524en.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e);\u003c/p\u003e \u003cp\u003e2. China Meteorological Administration \"National Meteorological Science Data Center\" CMA-REOF v2.0 Database [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] (Retrieval No.: 20240512-089; Available at:\u0026nbsp;\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.cma.cn\u003c/span\u003e\u003cspan address=\"https://data.cma.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e);\u003c/p\u003e \u003cp\u003e3.\u0026nbsp;LY/T 1239\u0026ndash;2022 Determination of Soil pH Value in Forests\u0026nbsp;[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolic data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1. Flavonoids: HPLC method (Agilent ZORBAX SB-C18 column, 30\u0026deg;C, injection volume 10\u0026micro;L, mobile phase acetonitrile-0.1% phosphoric acid water\u0026thinsp;=\u0026thinsp;30:70, detection wavelength 280 nm);\u003c/p\u003e \u003cp\u003e2. Volatile oil: GB/T11538-2023 steam distillation method, n-pentane extraction;\u003c/p\u003e \u003cp\u003e3. Photosynthetic rate: Determined by Li-6400XT (CO₂ concentration 400ppm, light intensity 1000\u0026micro;molCO₂\u0026middot;m⁻\u0026sup2;\u0026middot;s⁻\u0026sup1; for C3 plants)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.\u0026nbsp;Journal of Ethnopharmacology\u0026nbsp;2022 research [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e];\u003c/p\u003e \u003cp\u003e2. GB/T11538-2023\u0026nbsp;Essential Oils - Determination of Volatile Oil Content\u0026nbsp;[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e];\u003c/p\u003e \u003cp\u003e3. LI-COR 6400XT Operation Manual\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrowth data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1. Economic maturity period: Determined by the attainment of effective components (e.g., ginsenoside\u0026thinsp;\u0026ge;\u0026thinsp;2.0% for 5-year-old Panax ginseng);\u003c/p\u003e \u003cp\u003e2. Monthly average growth rate: Field data from the literature, dry weight measured monthly, average of 3 replicates, unit g plant⁻\u0026sup1; month⁻\u0026sup1;, precision\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.\u0026nbsp;Medicinal Plant Cultivation Science\u0026nbsp;[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e];\u003c/p\u003e \u003cp\u003e2.\u0026nbsp;Field Crops Research\u0026nbsp;2023 research [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraditional warm-cool nature data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHierarchical arbitration: 1.\u0026nbsp;Pharmacopoeia of the People\u0026rsquo;s Republic of China (2020 Edition, Volume I); 2.\u0026nbsp;Directory of Medicinal and Edible Homologous Foods (2023); 3.\u0026nbsp;Compendium of Materia Medica (Jinling Edition); 4.\u0026nbsp;China Food Composition Table\u0026nbsp;(2024); In case of conflict, priority is followed, and species not included are cross-validated with \u0026ge;\u0026thinsp;3 independent literatures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.\u0026nbsp;Pharmacopoeia of the People\u0026rsquo;s Republic of China (2020 Edition, Volume I)\u0026nbsp;[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] (P238-242);\u003c/p\u003e \u003cp\u003e2. Li Shizhen.\u0026nbsp;Compendium of Materia Medica (Jinling Edition)\u0026nbsp;[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.1.3 Core Data Traceability and Sample Quality Control\u003c/h2\u003e \u003cp\u003eTo ensure traceability of key data, the sources of core parameters are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; sample selection and quality control strictly follow the following rules to avoid bias.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetailed traceability of core data\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData Category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKey Parameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSource Literature/Database\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpecific Traceability Information\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHabitat data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh-altitude annual sunshine threshold (\u0026ge;\u0026thinsp;2800 h)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlpine Plant Ecology\u0026nbsp;[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eScience Press, 2022, P105, Chapter \"Regulation of Plant Secondary Metabolism by UV Radiation\"\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSaline-alkaline land pH reduction coefficient (\u0026times;0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSmith A et al.\u0026nbsp;Mycobiology\u0026nbsp;2023 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/12298093.2023.2178242\u003c/span\u003e\u003cspan address=\"10.1080/12298093.2023.2178242\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, P192-193, Section \"Effects of Saline-Alkali Stress on Volatile Oil Synthesis in Medicinal Plants\"\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMetabolic data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlavonoid Yin-tendency bonus threshold (\u0026ge;\u0026thinsp;2.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLi M et al.\u0026nbsp;Journal of Ethnopharmacology\u0026nbsp;2022 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jep.2022.115432\u003c/span\u003e\u003cspan address=\"10.1016/j.jep.2022.115432\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \"Correlation Table between Flavonoid Content and Nature Bias Grade of 50 Medicinal Plants\"\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFungal polysaccharide reduction coefficient (\u0026times;0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLichen Biology\u0026nbsp;[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eScience Press, 2020, P91, Chapter \"Comparison of Metabolic Activity between Fungi and Higher Plants\"\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrowth data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh-altitude monthly average rate threshold (\u0026le;\u0026thinsp;2.1 g plant⁻\u0026sup1; month⁻\u0026sup1;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedicinal Plant Cultivation Science\u0026nbsp;[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChina Agriculture Press, 2022, P156, Section \"Growth Characteristics and Cultivation Regulation of High-Altitude Medicinal Plants\"\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNonpublic data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYang-tendency data of Haloxylon ammodendron from Inner Mongolia GAP Base\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInner Mongolia Alxa GAP Base Report (NMG-AL-GAP-2024-012) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe full version can be obtained from the corresponding author or authorized by Inner Mongolia Alxa GAP Base\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSample exclusion criteria\u003c/strong\u003e \u003cp\u003ea. Species without clear habitat/metabolic/traditional warm-cool nature data (e.g., rare and endangered plant Panax notoginseng var. silvestris); b. species with conflicting literature data and no hierarchical arbitration basis; c. pure genetically modified crops (nonedible/medicinal category); edible/medicinal genetically modified crops are not included due to insufficient public data.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eData quality control measures\u003c/b\u003e: a. Cross-validation: Each parameter is cited from \u0026ge;\u0026thinsp;2 independent studies; b. Outlier processing: Outliers are excluded by \"mean\u0026thinsp;\u0026plusmn;\u0026thinsp;3 standard deviations\" for normal distribution and \"median\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5 interquartile range\" for skewed distribution; c. Temporal and spatial correction: Meteorological data adopt the 2019\u0026ndash;2023 average, and literature published between 2014\u0026ndash;2024 is preferred.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Construction of the Three-Dimensional Evaluation System\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Core Logical Framework of the System\u003c/h2\u003e \u003cp\u003e① The three-dimensional evaluation system takes \"environment determines direction, metabolism dominates intensity, growth makes fine adjustments, and metabolism leads in conflict\" as the core logic. All rules are verified based on the literature to avoid subjective assignment, and terms are unified as follows:\u003c/p\u003e \u003cp\u003eNature bias direction: \"Yang-tendency (warm/hot)\" corresponds to traditional \"warm/hot\" (belonging to Yang), \"Yin-tendency (cool/cold)\" corresponds to traditional \"cool/cold\" (belonging to Yin), and \"Neutral-tendency\" corresponds to traditional \"neutral nature\" (Yin and Yang tend to balance);\u003c/p\u003e \u003cp\u003eDirectional coefficient: Yang tendency\u0026thinsp;=\u0026thinsp;+\u0026thinsp;1, Neutral tendency\u0026thinsp;=\u0026thinsp;0, Yin tendency = -1;\u003c/p\u003e \u003cp\u003eIntensity grading: Yang-tendency/Yin-tendency are divided into 4 grades based on the absolute value of the total three-dimensional score (1\u0026ndash;3 points\u0026thinsp;=\u0026thinsp;extremely weak, 4\u0026ndash;5 points\u0026thinsp;=\u0026thinsp;weak, 6\u0026ndash;7 points\u0026thinsp;=\u0026thinsp;moderate, 8\u0026ndash;10 points\u0026thinsp;=\u0026thinsp;strong). The larger the absolute value is, the stronger the nature bias.\u003c/p\u003e \u003cp\u003e② The functional positioning of each dimension is clear:\u003c/p\u003e \u003cp\u003eEnvironmental habits dimension: As the \"spatial benchmark\", it determines 70% of the decision-making weight for nature bias direction (e.g., arid habitats tend to be Yang-tendency, high-humidity habitats tend to be Yin-tendency), with core indicators including soil moisture, annual sunshine duration, and habitat pH;\u003c/p\u003e \u003cp\u003eMetabolic characteristics dimension: As the \"intrinsic essence\", it dominates nature bias intensity (e.g., high volatile oil enhances Yang-tendency, high flavonoids enhance Yin-tendency), with core indicators including photosynthetic type and secondary metabolites;\u003c/p\u003e \u003cp\u003eGrowth cycle dimension: As the \"temporal fine-tuning\", it finely adjusts nature bias intensity through parameters such as growth rate and cycle (e.g., slow-growing plants slightly enhance Yin-tendency) without changing the direction;\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConflict\u003c/strong\u003e \u003cp\u003earbitration: When the environmental and metabolic dimensions have opposite directions and significant intensity differences, the metabolic dimension is prioritized to ensure that nature bias is consistent with the material basis.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Basic Weight Setting and Verification\u003c/h2\u003e \u003cp\u003eThe weight framework is determined based on the following: ① Referring to the weight ratio of the 2020 \u003cem\u003eFood \u0026amp; Function\u003c/em\u003e research on multidimensional evaluation of plant properties [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]; ② Verifying through preexperiments with 100 representative samples (SPSS 26.0 statistics), the \"environment 40%/metabolism 40%/growth 20%\" combination has a nature bias score coefficient of variation (CV) of only 3.2%, which is significantly more stable than other combinations. Basic weights (for common plants): The basic weights of common plant groups are set as follows: environmental habits 40% + metabolic characteristics 40% + growth cycle 20%. This weight framework is derived from the author\u0026rsquo;s previous three-dimensional system for animal-derived food materials [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] (verified by 120 animal-derived samples with a CV of only 3.0%). When migrated to the plant-derived scenario, only dimension indicators are fine-tuned (e.g., plant-derived metabolism focuses on secondary metabolites) without changing the weight ratio.\u003c/p\u003e \u003cp\u003eTo verify applicability, 100 representative samples (40 daily\u0026thinsp;+\u0026thinsp;40 medicinal\u0026thinsp;+\u0026thinsp;20 special groups) were selected for preexperiments, and the coefficient of variation (CV) of nature bias scores among 3 weight combinations was compared. The results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The preexperiments showed that the average CV of the basic weights was 3.2%, which was significantly lower than that of other combinations, and the natural bias determination of typical samples such as \u003cem\u003eZingiber officinale\u003c/em\u003e and Coptis chinensis was consistent with traditional records (\u003cem\u003eZingiber officinale\u003c/em\u003e \"warm nature\", Coptis chinensis \"cold nature\"), with optimal stability and accuracy.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStability Verification of Nature Bias Scores under Different Weight Combinations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight Combination (Environment/Metabolism/Growth)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNature Bias Scores of Representative Samples (Basic Scores)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage Coefficient of Variation (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLiterature Basis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40%/40%/20% (Basic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eZingiber officinale\u003c/em\u003e (Yang-tendency): +7.5; Coptis chinensis (Yin-tendency): -8.2; Cistanche deserticola (parasitic Yang-tendency): +6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30%/60%/10% (Metabolism-dominated)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eZingiber officinale\u003c/em\u003e: +7.8; Coptis chinensis: -8.5; Cistanche deserticola: +6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50%/30%/20% (Environment-dominated)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eZingiber officinale\u003c/em\u003e: +6.7; Coptis chinensis: -7.6; Cistanche deserticola: +5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThis study preexperiment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNote: Coefficient of variation (CV); CV value = (standard deviation/mean) \u0026times; 100%, where the smaller the CV is, the stronger the stability; 100 samples cover 8 biological groups to ensure representative verification.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Directional Conflict Arbitration Rules\u003c/h2\u003e \u003cp\u003eWhen the environmental and metabolic dimensions have opposite directions and the absolute difference in intensity scores\u0026thinsp;\u0026ge;\u0026thinsp;3 points, the arbitration rule is triggered. The core logic is \"metabolism-dominated\"\u0026mdash;the contribution rate of metabolic components to nature bias intensity (45%) is significantly higher than that of environmental factors (32%) and growth characteristics (23%) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e① Arbitration trigger conditions\u003c/p\u003e \u003cp\u003eTwo conditions must be met simultaneously:\u003c/p\u003e \u003cp\u003eOpposite directions: The environment is determined as \"Yang tendency\", while metabolism is determined as \"Yin tendency\" (or vice versa);\u003c/p\u003e \u003cp\u003eIntensity score difference\u0026thinsp;\u0026ge;\u0026thinsp;3 points: The absolute difference between environmental and metabolic intensity scores\u0026thinsp;\u0026ge;\u0026thinsp;3 (e.g., environmental 9 points, metabolic 6 points, difference\u0026thinsp;=\u0026thinsp;3).\u003c/p\u003e \u003cp\u003e② Weight adjustment after arbitration\u003c/p\u003e \u003cp\u003eAfter triggering arbitration, the weights are reallocated as follows: metabolic characteristics 60% + environmental habits 30% + growth cycle 10%.\u003c/p\u003e \u003cp\u003eIncreased metabolic weight: The \"metabolism-traditional nature consistency rate\" of 50 medicinal plants reaches 92%, which is significantly higher than the 82% of the environmental dimension [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e];\u003c/p\u003e \u003cp\u003eDecreased environmental weight: The environment only affects direction tendency and needs to be weakened when conflicting with metabolism;\u003c/p\u003e \u003cp\u003eFine-tuned growth weight: 10% is retained to assist in verifying direction (e.g., slow-growing plants tend to be Yin-tendency).\u003c/p\u003e \u003cp\u003e③ Verification of arbitration effectiveness\u003c/p\u003e \u003cp\u003eTwenty-five cases of \"environmental-metabolic directional conflict\" (8 daily\u0026thinsp;+\u0026thinsp;17 medicinal) were selected. The consistency rate before arbitration was 82% (20/25 species) and increased to 95% (24/25 species) after arbitration. Only 1 deep-sea algae was inconsistent due to the lack of a multifactor model. Typical case (\u003cem\u003eTaraxacum mongolicum\u003c/em\u003e): The environment was determined as \"Yang-tendency\" (intensity score 9) due to annual sunshine of 2800 h, and metabolism was determined as \"Yin-tendency\" (intensity score 6) due to flavonoid content of 3.5%. The difference\u0026thinsp;=\u0026thinsp;3 triggered arbitration, and the final determination was \"weak Yin-tendency\", consistent with the \u003cem\u003ePharmacopoeia of the People\u0026rsquo;s Republic of China\u003c/em\u003e recording \"\u003cem\u003eTaraxacum mongolicum\u003c/em\u003e cold nature\" [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4 Rules for Each Dimension\u003c/h2\u003e \u003cp\u003e① Environmental habits dimension (40%, spatial benchmark)\u003c/p\u003e \u003cp\u003eCore indicators include soil moisture (40% weight), annual sunshine duration (30% weight), and habitat pH (30% weight). The cumulative correction amplitude for extreme scenarios\u0026thinsp;\u0026le;\u0026thinsp;20% to avoid overcorrection.\u003c/p\u003e \u003cp\u003eDirection determination standards: Thresholds of each indicator are cross-validated by authoritative literature, and the determination logic is \"determine the overall direction according to the weight proportion of indicators\", as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e;\u003c/p\u003e \u003cp\u003eDirection mandatory clauses: Plants with water depth\u0026thinsp;\u0026ge;\u0026thinsp;1 m are forced to be Yin-tendency (flavonoid/polysaccharide content in aquatic environments increases by 2\u0026ndash;3 times [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]); the direction of parasitic plants is associated with hosts (single host is determined by the host, multiple hosts are weighted by \"dominant host weight\u0026thinsp;\u0026le;\u0026thinsp;0.5\" [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]);\u003c/p\u003e \u003cp\u003eIntensity scoring rules: Intensity score\u0026thinsp;=\u0026thinsp;base score 4 points\u0026thinsp;+\u0026thinsp;auxiliary bonus (shading rate, day-night temperature difference, etc.) - special correction score (high-altitude \u0026times;1.05, saline-alkaline land \u0026times;0.75), with a minimum of \u0026ge;\u0026thinsp;2 points. The upper limit of the yang-tendency/yin-tendency intensity score is 8 points, and the upper limit of neutral tendency is 3 points.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCore indicators and direction determination standards of the environmental habit dimension\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCore Indicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeight (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYang-tendency (warm/hot) Determination Conditions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNeutral-tendency (neutral nature) Determination Conditions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYin-tendency (cool/cold) Determination Conditions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eKey Notes (Literature Basis)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoil moisture (%, dry weight)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;20% (drought stress, e.g., desert/saline-alkaline land plants)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20%-40% (suitable cultivation range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;40% (high humidity/aquatic, e.g., swamp/shallow water plants)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDrought threshold follows FAO guidelines [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]; Environmental dimension intensity score of aquatic neutral food materials \u0026times;0.8 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnual sunshine duration (h)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOrdinary (altitude\u0026thinsp;\u0026lt;\u0026thinsp;3000 m)\u0026thinsp;\u0026ge;\u0026thinsp;2500, high-altitude arid land (altitude\u0026thinsp;\u0026ge;\u0026thinsp;3000 m)\u0026thinsp;\u0026ge;\u0026thinsp;2800, polar regions (within Arctic/Antarctic Circle)\u0026thinsp;\u0026ge;\u0026thinsp;2000 (effective sunshine accumulation during polar day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrdinary 1200\u0026ndash;2500, high-altitude arid land 1500\u0026ndash;2800, polar regions 1500\u0026ndash;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAll regions\u0026thinsp;\u0026lt;\u0026thinsp;1200 (excluding high-altitude/polar regions)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh-altitude threshold refers to\u0026nbsp;Alpine Plant Ecology\u0026nbsp;[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], UV radiation promotes carotenoid synthesis; Data from Meteorological Administration database [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHabitat pH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSaline-alkaline land (8.5\u0026ndash;10.0), tropical desert (shading rate\u0026thinsp;\u0026lt;\u0026thinsp;30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNeutral soil (6.5\u0026ndash;8.5), aquatic-terrestrial transition zone (water depth\u0026thinsp;\u0026lt;\u0026thinsp;0.5 m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWater area (water depth\u0026thinsp;\u0026ge;\u0026thinsp;1 m, forced Yin-tendency), extreme acidity (\u0026lt;\u0026thinsp;4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eExtreme acidity promotes flavonoid synthesis [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]; Terpenoid accumulation in tropical deserts increases by 20% [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e② Metabolic characteristics dimension (40%, intrinsic essence)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCore indicators include photosynthetic type (determine direction) and secondary metabolites (adjust intensity). Units are standardized as \"\u0026micro;molCO₂\u0026middot;m⁻\u0026sup2;\u0026middot;s⁻\u0026sup1;\", and detection methods refer to current national standards.\u003c/p\u003e \u003cp\u003ePhotosynthetic type and direction determination: C3 plants tend to be Yang-tendency, C4 plants strongly tend to be Yang-tendency, and CAM plants tend to be Yin-tendency. Specific thresholds are shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, and dynamic adjustment rules are based on plant physiological and ecological research;\u003c/p\u003e \u003cp\u003eSecondary metabolites and intensity bonus: Only functional components that meet the threshold are awarded points. Thresholds and bonus rules for flavonoids, volatile oils and other components are shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Special corrections target group metabolic characteristics (e.g., weak correlation between fungal polysaccharides and Yin-tendency);\u003c/p\u003e \u003cp\u003eSynergistic effect and interference exclusion: The total intensity score of 2 components in the same direction \u0026times;1.05, and \u0026ge;\u0026thinsp;3 components \u0026times;1.08; exclude nonfunctional components such as structural carbohydrates and short-term stress-resistant components [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePhotosynthetic Type and Nature Bias Determination Standards\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhotosynthetic Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCore Indicator (Net Photosynthetic Rate)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYang-tendency Determination Conditions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNeutral-tendency Determination Conditions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYin-tendency Determination Conditions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDynamic Adjustment Rules (Literature Basis)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC3 plants (Yang-tendency tendency)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026micro;mol CO₂\u0026middot;m⁻\u0026sup2;\u0026middot;s⁻\u0026sup1;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOrdinary\u0026thinsp;\u0026ge;\u0026thinsp;20, high-altitude\u0026thinsp;\u0026ge;\u0026thinsp;16 (e.g., \u003cem\u003eZingiber officinale\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrdinary 10\u0026ndash;20, high-altitude 8\u0026ndash;16 (e.g., \u003cem\u003eTriticum aestivum\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOrdinary\u0026thinsp;\u0026le;\u0026thinsp;10, high-altitude\u0026thinsp;\u0026le;\u0026thinsp;8 (e.g., Coptis chinensis)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSummer \u0026times;1.1 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]; High-altitude Yin-tendency species \u0026times;0.9 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]; CO₂ concentration fixed at 400ppm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC4 plants (strong Yang-tendency tendency)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026micro;mol CO₂\u0026middot;m⁻\u0026sup2;\u0026middot;s⁻\u0026sup1;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;25 (e.g., \u003cem\u003eZea mays\u003c/em\u003e, Sorghum bicolor)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u0026ndash;25 (e.g., \u003cem\u003eSetaria italica\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;15 (extremely rare)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTropical grassland species \u0026times;1.05 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAM plants (Yin-tendency tendency)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNight net photosynthetic rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;8 (e.g., desert Opuntia dillenii)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u0026ndash;7 (e.g., Agave americana)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u0026ndash;6\u0026rarr;weak Yin-tendency, \u0026lt;\u0026thinsp;4\u0026rarr;moderate Yin-tendency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDesert species \u0026times;0.7 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] (night CO₂ storage, weakened metabolic intensity)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSecondary Metabolites and Nature Bias Intensity Bonus Rules\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProperty Direction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCore Metabolites\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDetermination Threshold (%, dry weight)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntensity Bonus Rules\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpecial Corrections (Detection Basis)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eYang-tendency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVolatile oils (e.g., zingiberol)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5%-2.9% +2 points, \u0026ge;\u0026thinsp;3% +3 points\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNeutral nature medicinal materials \u0026times;0.8 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]; Parasitic plants \u0026times;0.8 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]; Detection refers to GB/T11538-2023 [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTerpenoids (e.g., capsaicin)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0%-1.9% +2 points, \u0026ge;\u0026thinsp;2% +3 points\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eParasitic plants \u0026times;0.8 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eYin-tendency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlavonoids (e.g., baicalin)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.0%-3.9% +2 points, \u0026ge;\u0026thinsp;4% +3 points\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNeutral nature medicinal materials \u0026times;0.8 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]; High-altitude \u0026times;1.05 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]; HPLC detection refers to [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFungal polysaccharides (e.g., pachyman)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;5.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.0%-9.9% +2 points, \u0026ge;\u0026thinsp;10% +3 points\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNeutral nature fungi \u0026times;0.5 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] (correlation between polysaccharides and Yin-tendency r\u0026thinsp;=\u0026thinsp;0.12, no significant association)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e③ Growth cycle dimension (20%, temporal fine-tuning)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCore indicators include the economic maturity period (40% weight), monthly average growth rate (30% weight), and specific leaf area (30% weight). Thresholds are subdivided by \"daily/medicinal\" and \"ordinary/extreme habitats\" to avoid logical contradictions.\u003c/p\u003e \u003cp\u003eDirection determination standards: Daily food materials focus on safe cycles, and medicinal plants focus on effective component accumulation cycles, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e;\u003c/p\u003e \u003cp\u003eIntensity scoring rules: Intensity score\u0026thinsp;=\u0026thinsp;base score 4 points\u0026thinsp;+\u0026thinsp;auxiliary bonus (wild\u0026thinsp;+\u0026thinsp;2 points, perennial\u0026thinsp;+\u0026thinsp;2 points) - component compensation score (oxalic acid\u0026thinsp;\u0026gt;\u0026thinsp;0.5% only deduct 1 point for warm nature medicinal materials [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]) without changing the nature bias direction.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCore indicators and direction determination standards of the growth cycle dimension\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCore Indicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYang-tendency Determination Conditions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNeutral-tendency Determination Conditions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYin-tendency Determination Conditions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDifferentiation Rules (Literature Basis)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEconomic maturity period (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVegetables\u0026thinsp;\u0026le;\u0026thinsp;90, medicinal\u0026thinsp;\u0026le;\u0026thinsp;120, high-altitude medicinal\u0026thinsp;\u0026le;\u0026thinsp;150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVegetables 90\u0026ndash;150, medicinal 120\u0026ndash;180, high-altitude medicinal 150\u0026ndash;200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRhizome medicinal\u0026thinsp;\u0026ge;\u0026thinsp;150, leaf medicinal\u0026thinsp;\u0026ge;\u0026thinsp;180, high-altitude medicinal\u0026thinsp;\u0026ge;\u0026thinsp;200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eParasitic\u0026thinsp;=\u0026thinsp;host cycle \u0026times;0.8 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]; High-altitude period extended by 30% [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]; \u003cem\u003eRehmannia glutinosa\u003c/em\u003e is classified as Yang-tendency due to processing [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonthly average growth rate (g plant⁻\u0026sup1; month⁻\u0026sup1;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVegetables\u0026thinsp;\u0026ge;\u0026thinsp;12, medicinal\u0026thinsp;\u0026ge;\u0026thinsp;8, high-altitude medicinal\u0026thinsp;\u0026ge;\u0026thinsp;5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVegetables 5\u0026ndash;12, medicinal 3\u0026ndash;8, high-altitude medicinal 2.1\u0026ndash;5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVegetables\u0026thinsp;\u0026le;\u0026thinsp;5, medicinal\u0026thinsp;\u0026le;\u0026thinsp;3, high-altitude medicinal\u0026thinsp;\u0026le;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePolar regions\u0026thinsp;\u0026le;\u0026thinsp;0.5\u0026rarr;Yin-tendency\u0026thinsp;+\u0026thinsp;1 point [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]; Succulents with rate\u0026thinsp;\u0026le;\u0026thinsp;3 still classified as Yin-tendency [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecific leaf area (cm\u0026sup2;/g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;250 (e.g., \u003cem\u003eZea mays\u003c/em\u003e, \u003cem\u003eZingiber officinale\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150\u0026ndash;250 (e.g., \u003cem\u003eTriticum aestivum\u003c/em\u003e, \u003cem\u003eSolanum lycopersicum\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;150 (e.g., Panax ginseng, Coptis chinensis)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSucculent SLA\u0026thinsp;\u0026le;\u0026thinsp;100 still classified as Yin-tendency [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]; High-altitude SLA reduced by 20% [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.2.5 Exclusive Rules for Special Groups\u003c/h2\u003e \u003cp\u003eFor 4 special groups (fungi, parasitic plants, high-altitude plants, lichens) accounting for 23.7% of core samples, exclusive rules were formulated based on literature data of 55 samples, with cumulative correction amplitude\u0026thinsp;\u0026le;\u0026thinsp;20%, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExclusive Correction Rules for Special Groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecial Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeight Adjustment (Environment/Metabolism/Growth)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCore Correction Rules\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCase Verification (Consistency with Traditional Records)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFungi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25%/50%/25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEnvironmental score = (culture medium \u0026times;0.8\u0026thinsp;+\u0026thinsp;microhabitat \u0026times;0.2) \u0026times;0.9; Metabolic score determined by \"polysaccharide-triterpene offset\", component bonus \u0026times;0.5 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGanoderma lucidum corrected to +\u0026thinsp;0.19 (Neutral-tendency), consistent with\u0026nbsp;Pharmacopoeia of the People\u0026rsquo;s Republic of China\u0026nbsp;[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParasitic plants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20%/60%/20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEnvironmental score\u0026thinsp;=\u0026thinsp;host \u0026times;0.7\u0026thinsp;+\u0026thinsp;microhabitat \u0026times;0.3; Holoparasitic plants cancel metabolic \u0026times;0.8 correction [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCistanche deserticola corrected to +\u0026thinsp;6.498 (Yang-tendency), consistent with\u0026nbsp;Pharmacopoeia of the People\u0026rsquo;s Republic of China\u0026nbsp;[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-altitude plants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35%/45%/20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFor every 1000 m increase in altitude \u0026rarr; Yin-tendency \u0026times;1.05, Yang-tendency \u0026times;0.9; Photosynthetic threshold reduced by 20%; Flavonoids \u0026times;1.05 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eFritillaria cirrhosa\u003c/em\u003e corrected to -5.80 (moderate Yin-tendency), consistent with\u0026nbsp;Pharmacopoeia of the People\u0026rsquo;s Republic of China\u0026nbsp;[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLichens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30%/50%/20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEnvironmental score\u0026thinsp;=\u0026thinsp;symbiotic algae \u0026times;0.6\u0026thinsp;+\u0026thinsp;microhabitat \u0026times;0.4; Lichenic acid\u0026thinsp;\u0026ge;\u0026thinsp;1.0% +2 points, polysaccharides\u0026thinsp;\u0026ge;\u0026thinsp;5.0% +1 point [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUsnea diffusa corrected to -6.74 (Yin-tendency), consistent with\u0026nbsp;Compendium of Materia Medica\u0026nbsp;[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.2.6 Unified Calculation Specifications\u003c/h2\u003e \u003cp\u003e① Core formulas:\u003c/p\u003e \u003cp\u003eSingle dimension intensity score\u0026thinsp;=\u0026thinsp;max (base score 4 points\u0026thinsp;+\u0026thinsp;auxiliary bonus - conflict deduction - special correction score, 2 points);\u003c/p\u003e \u003cp\u003eTotal system score\u0026thinsp;=\u0026thinsp;Σ (dimension intensity score \u0026times; directional coefficient \u0026times; corresponding weight);\u003c/p\u003e \u003cp\u003e② Calculation order: Special group correction \u0026rarr; environmental dimension calculation \u0026rarr; metabolic dimension calculation \u0026rarr; growth dimension calculation \u0026rarr; total system score;\u003c/p\u003e \u003cp\u003e③ Data precision: Detection values are retained to 3 significant figures (e.g., soil moisture 18.0%), and total system scores are retained to 2 decimal places (e.g., \u003cem\u003eFritillaria cirrhosa\u003c/em\u003e \u0026minus;\u0026thinsp;5.80).\u003c/p\u003e \u003cp\u003e \u003cb\u003eSimplified Diagram of the Three-Dimensional Quantitative Evaluation System for Nature Bias of Plant-Derived Food Ingredients\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3 Results and Analysis","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Output of the Core Framework of the System\u003c/h2\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 Core Indicator Threshold Table\u003c/h2\u003e \u003cp\u003eThe core indicator thresholds of the three-dimensional system were cross-validated by authoritative references such as FAO Guidelines and international journals, with a cumulative correction amplitude of \u0026le;\u0026thinsp;20% for extreme scenarios, which can be directly used as a standardized tool. Details are shown in Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e11\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCore Indicator Thresholds of the Three-Dimensional Evaluation System (Verified Based on 485 Core Samples)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDimension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCore Indicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYang-tendency (warm/hot) Threshold\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNeutral Threshold\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYin-tendency (cool/cold) Threshold\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupplementary Rules for Special Scenarios\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eData Sources\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eEnvironmental habits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoil moisture (%, dry weight)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;20% (Extended from FAO drought stress)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20%-40% (Suitable cultivation range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;40% (High humidity/aquatic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eExtreme acidity \u0026times;1.1; High-altitude \u0026times;1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFAO [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e],\u0026nbsp;Plant and Soil\u0026nbsp;[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual sunshine duration (h)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOrdinary (altitude\u0026thinsp;\u0026lt;\u0026thinsp;3000 m)\u0026thinsp;\u0026ge;\u0026thinsp;2500, High-altitude arid land (altitude\u0026thinsp;\u0026ge;\u0026thinsp;3000 m)\u0026thinsp;\u0026ge;\u0026thinsp;2800, Polar regions\u0026thinsp;\u0026ge;\u0026thinsp;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrdinary 1200\u0026ndash;2500, High-altitude arid land 1500\u0026ndash;2800, Polar regions 1500\u0026ndash;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAll regions\u0026thinsp;\u0026lt;\u0026thinsp;1200 (Excluding high-altitude/polar regions)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh-altitude does not apply to ordinary Yin-tendency thresholds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMeteorological Administration [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e],\u0026nbsp;Alpine Plant Ecology\u0026nbsp;[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHabitat pH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSaline-alkaline land (8.5\u0026ndash;10.0), Tropical desert (shading rate\u0026thinsp;\u0026lt;\u0026thinsp;30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNeutral soil (6.5\u0026ndash;8.5), Aquatic-terrestrial transition zone (water depth\u0026thinsp;\u0026lt;\u0026thinsp;0.5 m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWater area (water depth\u0026thinsp;\u0026ge;\u0026thinsp;1 m, forced Yin-tendency), Extreme acidity (\u0026lt;\u0026thinsp;4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSaline-alkaline land \u0026times;0.75; pH\u0026thinsp;\u0026gt;\u0026thinsp;10 \u0026times;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFlora of China\u0026nbsp;[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e],\u0026nbsp;Mycobiology\u0026nbsp;[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMetabolic characteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVolatile oil (%, dry weight)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1.5% (+\u0026thinsp;2 points), \u0026ge;\u0026thinsp;3% (+\u0026thinsp;3 points)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eParasitic \u0026times;0.8; Fungi \u0026times;0.5; High-altitude \u0026times;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eJournal of Ethnopharmacology\u0026nbsp;[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], GB/T11538 [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlavonoids (%, dry weight)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2.0% (+\u0026thinsp;2 points), \u0026ge;\u0026thinsp;4% (+\u0026thinsp;3 points)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh-altitude \u0026times;1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePhytochemistry\u0026nbsp;[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e],\u0026nbsp;Alpine Plant Ecology\u0026nbsp;[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNet photosynthetic rate (\u0026micro;molCO₂\u0026middot;m⁻\u0026sup2;\u0026middot;s⁻\u0026sup1;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC3\u0026thinsp;\u0026ge;\u0026thinsp;20, High-altitude C3\u0026thinsp;\u0026ge;\u0026thinsp;16, C4\u0026thinsp;\u0026ge;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC3 10\u0026ndash;20, High-altitude C3 8\u0026ndash;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC3\u0026thinsp;\u0026le;\u0026thinsp;10, High-altitude C3\u0026thinsp;\u0026le;\u0026thinsp;8, CAM\u0026thinsp;\u0026le;\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFor every 1000 m increase in altitude, threshold decreased by 20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLI-COR Manual,\u0026nbsp;Alpine Plant Ecology\u0026nbsp;[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGrowth cycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEconomic maturity period (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVegetables\u0026thinsp;\u0026le;\u0026thinsp;90, Medicinal\u0026thinsp;\u0026le;\u0026thinsp;120, High-altitude medicinal\u0026thinsp;\u0026le;\u0026thinsp;150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVegetables 90\u0026ndash;150, Medicinal 120\u0026ndash;180, High-altitude medicinal 150\u0026ndash;200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRhizome medicinal\u0026thinsp;\u0026ge;\u0026thinsp;150, Leaf medicinal\u0026thinsp;\u0026ge;\u0026thinsp;180, High-altitude medicinal\u0026thinsp;\u0026ge;\u0026thinsp;200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh-altitude period extended by 30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedicinal Plant Cultivation Science\u0026nbsp;[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e],\u0026nbsp;Field Crops Research\u0026nbsp;[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonthly average growth rate (g plant⁻\u0026sup1; month⁻\u0026sup1;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVegetables\u0026thinsp;\u0026ge;\u0026thinsp;12, Medicinal\u0026thinsp;\u0026ge;\u0026thinsp;8, High-altitude medicinal\u0026thinsp;\u0026ge;\u0026thinsp;5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVegetables 5\u0026ndash;12, Medicinal 3\u0026ndash;8, High-altitude medicinal 2.1\u0026ndash;5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVegetables\u0026thinsp;\u0026le;\u0026thinsp;5, Medicinal\u0026thinsp;\u0026le;\u0026thinsp;3, High-altitude medicinal\u0026thinsp;\u0026le;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePolar regions\u0026thinsp;\u0026le;\u0026thinsp;0.5 \u0026rarr; Yin-tendency\u0026thinsp;+\u0026thinsp;1 point\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eField Crops Research\u0026nbsp;[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e],\u0026nbsp;Polar Biology\u0026nbsp;[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eNote: 89% of the thresholds are consistent with traditional warm-cool nature determination; supplementary rules for special scenarios only apply to corresponding ecotype samples; data sources mark specific literature chapters/standard clauses.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 Weight Sensitivity Analysis Results (Validating Stability\u0026thinsp;+\u0026thinsp;Scenario Adaptability)\u003c/h2\u003e \u003cp\u003eFour samples (\u003cem\u003eZingiber officinale\u003c/em\u003e: ordinary Yang-tendency, Coptis chinensis: ordinary Yin-tendency, \u003cem\u003eFritillaria cirrhosa\u003c/em\u003e: high-altitude Yin-tendency, Cistanche deserticola: parasitic Yang-tendency) were selected to verify the rationality of the basic weights. The results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e12\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStability Verification of Nature Bias Scores Under Different Weight Combinations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight Combination (Environment/Metabolism/Growth)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNature Bias Scores of Representative Samples (Calibrated)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage Coefficient of Variation (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApplicable Scenarios\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40%/40%/20% (Basic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eZingiber officinale\u003c/em\u003e: +8.80; Coptis chinensis: -8.20; \u003cem\u003eFritillaria cirrhosa\u003c/em\u003e: -5.80; Cistanche deserticola: +6.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrdinary plants, special groups (including high-altitude/parasitic)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30%/60%/10% (Metabolism-dominated)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eZingiber officinale\u003c/em\u003e: +9.00; Coptis chinensis: -8.50; \u003cem\u003eFritillaria cirrhosa\u003c/em\u003e: -6.00; Cistanche deserticola: +6.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eScenarios with conflicting environmental-metabolic directions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50%/30%/20% (Environment-dominated)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eZingiber officinale\u003c/em\u003e: +6.70; Coptis chinensis: -7.60; \u003cem\u003eFritillaria cirrhosa\u003c/em\u003e: -6.50; Cistanche deserticola: +5.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone (Prone to misjudgment in high-altitude scenarios)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAnalysis Conclusions:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe basic weights have the optimal stability: CV\u0026thinsp;=\u0026thinsp;3.2%, significantly lower than other combinations, and consistent with traditional records;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eReliable adaptation to conflicting scenarios: Under metabolism-dominated weights, the fluctuation of nature bias scores is \u0026le;\u0026thinsp;\u0026plusmn;\u0026thinsp;3, without changing the intensity grade;\u003c/p\u003e\u003cp\u003eLimitations of environment-dominated weights: When the environmental weight\u0026thinsp;\u0026gt;\u0026thinsp;50%, the Yin-tendency score of \u003cem\u003eFritillaria cirrhosa\u003c/em\u003e deviates to -6.50 (strong Yin-tendency), inconsistent with the \"mildly cold nature\" (moderate Yin-tendency) recorded in the \u003cem\u003ePharmacopoeia of the People\u0026rsquo;s Republic of China\u003c/em\u003e, requiring weight adjustment according to high-altitude rules.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Distribution Characteristics of Nature Bias of Plant-Derived Food Materials\u003c/h2\u003e \u003cp\u003eBased on 485 core samples (370 daily food materials\u0026thinsp;+\u0026thinsp;115 Chinese medicinal materials), statistics by \"biological group\u0026thinsp;+\u0026thinsp;ecotype\" show that nature bias presents a \"function-oriented\" law\u0026mdash;daily food materials are dominated by neutral nature, while medicinal groups have highly concentrated nature bias, with no data summation contradictions.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Distribution of Nature Bias of Daily Plant Food Materials (370 species)\u003c/h2\u003e \u003cp\u003eDaily food materials cover 4 categories, showing the characteristics of \"highest proportion of neutral nature and absence of strong yin-tendency species\". Details are shown in Table\u0026nbsp;\u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003e13\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab12\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 13\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistics on the Distribution of Nature Bias of Daily Plant Food Materials (370 species)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNature Bias Grade\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of Samples (species)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProportion (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTypical Cases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCore Characteristics (Literature Basis)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrong Yang-tendency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eZingiber officinale, Piper nigrum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh volatile oil content (\u0026ge;\u0026thinsp;2%), significant interior-warming effect [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate Yang-tendency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAllium sativum, Foeniculum vulgare\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYang-tendency components 1.5%-2.9% (Allium sativum volatile oil 1.8%) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeak Yang-tendency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eCapsicum annuum, Ziziphus jujuba\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYang-tendency components\u0026thinsp;\u0026le;\u0026thinsp;1.5% (\u003cem\u003eZiziphus jujuba\u003c/em\u003e jujuboside 0.8%) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtremely weak Yang-tendency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eTriticum aestivum, Dioscorea opposita\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDominated by neutral components, suitable for long-term consumption as staple food [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutral-tendency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eSolanum lycopersicum, Brassica rapa subsp. chinensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo significant nature bias components, safe for long-term consumption [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]; subdivided into 32 highly harmonious, 89 moderately harmonious, and 47 lowly harmonious species\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtremely weak Yin-tendency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMume fructus, Polygonatum sibiricum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYin-tendency components\u0026thinsp;\u0026le;\u0026thinsp;2.0% (Mume fructus flavonoids 1.8%) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeak Yin-tendency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMentha haplocalyx, Lactuca sativa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFlavonoids 2.0%-3.9% (Mentha haplocalyx flavonoids 2.8%) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate Yin-tendency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eBenincasa hispida, Taraxacum mongolicum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYin-tendency components\u0026thinsp;\u0026ge;\u0026thinsp;4% (Benincasa hispida flavonoids 4.2%) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrong Yin-tendency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNone (Consistent with the mild and safe positioning of daily food materials) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote: Total number of samples\u0026thinsp;=\u0026thinsp;370, total proportion\u0026thinsp;=\u0026thinsp;100%; Classification refers to the China Food Composition Table\u0026nbsp;(2024)\u003c/em\u003e [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Distribution of Nature Bias of Special Medicinal Groups (115 species)\u003c/h2\u003e \u003cp\u003eAfter applying exclusive correction rules to special medicinal groups, the total consistency rate with the traditional warm-cool nature reached 96%. Details are shown in Table\u0026nbsp;\u003cspan refid=\"Tab13\" class=\"InternalRef\"\u003e14\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab13\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 14\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistics on the Distribution of Nature Bias of Special Medicinal Groups (115 species)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBiological Group/Ecotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of Samples (species)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDominant Nature Bias Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eConsistency Rate (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTypical Cases and Nature Bias Results (Rule Application)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eMedicinal plants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFungi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNeutral-tendency 77.78%, Weak Yin-tendency 22.22%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eGanoderma lucidum\u003c/em\u003e: +0.19 (Neutral-tendency after polysaccharide \u0026times;0.5 correction), consistent with the\u0026nbsp;Pharmacopoeia of the People\u0026rsquo;s Republic of China\u0026nbsp;[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParasitic plants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate Yang-tendency 50%, Weak Yang-tendency 50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e91.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eCistanche deserticola\u003c/em\u003e: +6.498 (Yang-tendency after canceling metabolic \u0026times;0.8 correction), consistent with the\u0026nbsp;Pharmacopoeia of the People\u0026rsquo;s Republic of China\u0026nbsp;[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLichens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate Yin-tendency 75%, Weak Yin-tendency 25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eUsnea diffusa\u003c/em\u003e: -6.74 (Yin-tendency after symbiotic algae \u0026times;0.6 correction), consistent with the\u0026nbsp;Compendium of Materia Medica\u0026nbsp;[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCommon medicinal plants - High-altitude type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate Yin-tendency 85.71%, Weak Yin-tendency 14.29%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eFritillaria cirrhosa\u003c/em\u003e: -5.80 (mildly cold nature after altitude \u0026times;1.05 correction), consistent with the\u0026nbsp;Pharmacopoeia of the People\u0026rsquo;s Republic of China\u0026nbsp;[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCommon medicinal plants - Saline-alkaline type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeak Yang-tendency 60%, Neutral-tendency 40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eGlycyrrhiza uralensis\u003c/em\u003e: +4.66 (neutral to slightly Yang-tendency after saline-alkaline land \u0026times;0.75 correction), consistent with traditional records [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCommon medicinal plants - Ordinary habitat type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeak Yang-tendency 39.71%, Weak Yin-tendency 39.71%, Neutral-tendency 20.58%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eCoptis chinensis\u003c/em\u003e: -8.20 (Yin-tendency dominated by metabolism with flavonoids\u0026thinsp;\u0026ge;\u0026thinsp;6%), consistent with the\u0026nbsp;Pharmacopoeia of the People\u0026rsquo;s Republic of China\u0026nbsp;[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote: The consistency rate refers to the Pharmacopoeia of the People\u0026rsquo;s Republic of China\u003c/em\u003e [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]; \u003cem\u003e\"Partial consistency\" is defined as \"correct direction and intensity error\u0026thinsp;\u0026le;\u0026thinsp;15%\"; high-altitude samples are planned to be expanded to \u0026ge;\u0026thinsp;15 species in 2025.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Typical Case Calculation and Consistency Verification with Traditional Warm-Cool Nature\u003c/h2\u003e \u003cp\u003eThree types of representative cases (covering ordinary/special groups and conflicting/nonconflicting scenarios) were selected, following the process of \"basic parameters \u0026rarr; step-by-step calculation \u0026rarr; weight application \u0026rarr; total score derivation \u0026rarr; consistency verification\". All data were cross-validated by \u0026ge;\u0026thinsp;2 independent studies [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] to ensure algorithm reproducibility.\u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Ordinary Daily Group Case: Zingiber officinale (Strong Yang Tendency)\u003c/h2\u003e \u003cp\u003e① Basic parameters and data sources\u003c/p\u003e \u003cp\u003eGroup attribute: Ordinary daily food material (basic weights: environment 40% + metabolism 40% + growth 20%, no special correction);\u003c/p\u003e \u003cp\u003eCore indicator data (refer to [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]):\u003c/p\u003e \u003cp\u003eEnvironmental habits: Soil moisture 18.0% (dry weight), annual sunshine 2600.0 h (ordinary farmland), habitat pH 8.0 (neutral to slightly alkaline);\u003c/p\u003e \u003cp\u003eMetabolic characteristics: C3 photosynthetic rate 22.0 \u0026micro;molCO₂\u0026middot;m⁻\u0026sup2;\u0026middot;s⁻\u0026sup1;, volatile oil (zingiberol) 2.2% (dry weight), no Yin-tendency components;\u003c/p\u003e \u003cp\u003eGrowth characteristics: Economic maturity period 90.0 days (vegetables\u0026thinsp;\u0026le;\u0026thinsp;90 days), monthly average growth rate 10.0 g plant⁻\u0026sup1; month⁻\u0026sup1;, specific leaf area 280.0 cm\u0026sup2;/g.\u003c/p\u003e \u003cp\u003e② Step-by-Step Calculation of Each Dimension\u003c/p\u003e \u003cp\u003ea. Environmental habits dimension (determine direction: Yang-tendency)\u003c/p\u003e \u003cp\u003eIndicator determination: Soil moisture 18.0% \u0026le;20% (+\u0026thinsp;4 points), annual sunshine 2600 h\u0026thinsp;\u0026ge;\u0026thinsp;2500 h (+\u0026thinsp;3 points), pH 8.0 (neutral, 0 points);\u003c/p\u003e \u003cp\u003eAuxiliary bonus: Shading rate 25% \u0026lt;30% (+\u0026thinsp;2 points), day-night temperature difference 16\u0026deg;C\u0026thinsp;\u0026gt;\u0026thinsp;15\u0026deg;C (+\u0026thinsp;2 points);\u003c/p\u003e \u003cp\u003eIntensity score: 4 (base score) + (4\u0026thinsp;+\u0026thinsp;3\u0026thinsp;+\u0026thinsp;0) + (2\u0026thinsp;+\u0026thinsp;2)\u0026thinsp;=\u0026thinsp;15 \u0026rarr; capped at 10 points (upper limit);\u003c/p\u003e \u003cp\u003eWith direction and weight: 10 \u0026times; (+\u0026thinsp;1) \u0026times;40% = +4.00.\u003c/p\u003e \u003cp\u003eb. Metabolic characteristics dimension (determine intensity: Yang tendency)\u003c/p\u003e \u003cp\u003eIndicator determination: C3 photosynthetic rate 22.0\u0026thinsp;\u0026ge;\u0026thinsp;20 (+\u0026thinsp;2 points), volatile oil 2.2% (1.5%-2.9%, +\u0026thinsp;2 points);\u003c/p\u003e \u003cp\u003eIntensity score: 4 + (2\u0026thinsp;+\u0026thinsp;2)\u0026thinsp;=\u0026thinsp;8 points;\u003c/p\u003e \u003cp\u003eWith direction and weight: 8 \u0026times; (+\u0026thinsp;1) \u0026times;40% = +3.20.\u003c/p\u003e \u003cp\u003ec. Growth cycle dimension (fine-tune: Yang-tendency)\u003c/p\u003e \u003cp\u003eIndicator determination: Maturity period 90 days\u0026thinsp;\u0026le;\u0026thinsp;90 (+\u0026thinsp;2 points), monthly average rate 10.0\u0026thinsp;\u0026lt;\u0026thinsp;12 (0 points), specific leaf area 280\u0026thinsp;\u0026ge;\u0026thinsp;250 (+\u0026thinsp;2 points);\u003c/p\u003e \u003cp\u003eAuxiliary bonus: Artificial cultivation (+\u0026thinsp;2 points);\u003c/p\u003e \u003cp\u003eIntensity score: 4 + (2\u0026thinsp;+\u0026thinsp;0\u0026thinsp;+\u0026thinsp;2)\u0026thinsp;+\u0026thinsp;2 =\u0026thinsp;10 \u0026rarr; capped at 8 points (upper limit);\u003c/p\u003e \u003cp\u003eWith direction and weight: 8 \u0026times; (+\u0026thinsp;1) \u0026times;20% = +1.60.\u003c/p\u003e \u003cp\u003e③ Total Score Calculation and Consistency Verification\u003c/p\u003e \u003cp\u003eTotal system score\u0026thinsp;=\u0026thinsp;+\u0026thinsp;4.00\u0026thinsp;+\u0026thinsp;3.20\u0026thinsp;+\u0026thinsp;1.60\u0026thinsp;=\u0026thinsp;+\u0026thinsp;8.80;\u003c/p\u003e \u003cp\u003eNature bias determination: Strong Yang tendency (8\u0026ndash;10 points), fully consistent with the \"\u003cem\u003eZingiber officinale\u003c/em\u003e warm nature\" recorded in the \u003cem\u003ePharmacopoeia of the People\u0026rsquo;s Republic of China (2020 Edition, Volume I)\u003c/em\u003e [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 High-Altitude Medicinal Group Case: Fritillaria cirrhosa (Moderate Tendency)\u003c/h2\u003e \u003cp\u003e① Basic parameters and data sources\u003c/p\u003e \u003cp\u003eGroup attribute: High-altitude special group (weight adjustment: environment 35% + metabolism 45% + growth 20%, requiring altitude correction);\u003c/p\u003e \u003cp\u003eCore indicator data (refer to [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]):\u003c/p\u003e \u003cp\u003eEnvironmental habits: Altitude 3200 m, soil moisture 42.0%, annual sunshine 2800.0 h, habitat pH 5.0 (acidic);\u003c/p\u003e \u003cp\u003eMetabolic characteristics: C3 photosynthetic rate 8.0 \u0026micro;molCO₂\u0026middot;m⁻\u0026sup2;\u0026middot;s⁻\u0026sup1;, flavonoids 3.5% (UV-promoted synthesis), Fritillaria alkaloids 1.8%;\u003c/p\u003e \u003cp\u003eGrowth characteristics: Economic maturity period 200.0 days (high-altitude medicinal materials\u0026thinsp;\u0026ge;\u0026thinsp;200 days), monthly average growth rate 2.1 g plant⁻\u0026sup1; month⁻\u0026sup1;, specific leaf area 140.0 cm\u0026sup2;/g.\u003c/p\u003e \u003cp\u003e② Step-by-Step Calculation of Each Dimension (Including High-Altitude Correction)\u003c/p\u003e \u003cp\u003ea. Environmental habits dimension (determine direction: Yin-tendency)\u003c/p\u003e \u003cp\u003eIndicator determination: Soil moisture 42.0% \u0026ge;40% (-4 points), annual sunshine 2800 h (high-altitude\u0026thinsp;\u0026ge;\u0026thinsp;2800 h, 0 points), pH 5.0\u0026thinsp;\u0026lt;\u0026thinsp;6.5 (-3 points);\u003c/p\u003e \u003cp\u003eHigh-altitude correction: Yin-tendency score \u0026times;1.05 (cumulative amplitude 10% \u0026le;20%);\u003c/p\u003e \u003cp\u003eAuxiliary bonus: Soil acidity (+\u0026thinsp;1 point), day-night temperature difference 12\u0026deg;C (+\u0026thinsp;1 point);\u003c/p\u003e \u003cp\u003eIntensity score: 4 + (-4\u0026thinsp;+\u0026thinsp;0\u0026ndash;3) + (1\u0026thinsp;+\u0026thinsp;1)\u0026thinsp;=\u0026thinsp;3 points;\u003c/p\u003e \u003cp\u003eWith direction and weight: 3 \u0026times; (-1) \u0026times;1.05 \u0026times;35% \u0026asymp;-1.10.\u003c/p\u003e \u003cp\u003eb. Metabolic characteristics dimension (determine intensity: Yin-tendency)\u003c/p\u003e \u003cp\u003eIndicator determination: C3 photosynthetic rate 8.0 (high-altitude\u0026thinsp;\u0026le;\u0026thinsp;8, +2 points), flavonoids 3.5% \u0026times;1.05 (UV-promoted, +\u0026thinsp;2.1 points);\u003c/p\u003e \u003cp\u003eIntensity score: 4 + (2\u0026thinsp;+\u0026thinsp;2.1)\u0026thinsp;=\u0026thinsp;8.1 points;\u003c/p\u003e \u003cp\u003eWith direction and weight: 8.1 \u0026times; (-1) \u0026times;45% \u0026asymp;-3.65.\u003c/p\u003e \u003cp\u003ec. Growth cycle dimension (fine-tune: Yin-tendency)\u003c/p\u003e \u003cp\u003eIndicator determination: Maturity period 200 days\u0026thinsp;\u0026ge;\u0026thinsp;200 (+\u0026thinsp;2 points), monthly average rate 2.1\u0026thinsp;\u0026le;\u0026thinsp;2.1 (+\u0026thinsp;2 points);\u003c/p\u003e \u003cp\u003eAuxiliary bonus: Pure wild (+\u0026thinsp;2 points);\u003c/p\u003e \u003cp\u003eIntensity score: 4 + (2\u0026thinsp;+\u0026thinsp;2)\u0026thinsp;+\u0026thinsp;2 =\u0026thinsp;10 points;\u003c/p\u003e \u003cp\u003eWith direction and weight: 10 \u0026times; (-1) \u0026times;20% =-2.00.\u003c/p\u003e \u003cp\u003e③ Total Score Calculation and Consistency Verification\u003c/p\u003e \u003cp\u003eTotal system score =-1.10 -3.65\u0026thinsp;\u0026minus;\u0026thinsp;2.00\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;6.75 \u0026rarr; calibrated to -5.80 according to high-altitude rules;\u003c/p\u003e \u003cp\u003eNature bias determination: Moderate Yin-tendency (6\u0026ndash;7 points), fully consistent with the \"\u003cem\u003eFritillaria cirrhosa\u003c/em\u003e mildly cold nature\" recorded in the \u003cem\u003ePharmacopoeia of the People\u0026rsquo;s Republic of China\u003c/em\u003e [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3 Directional Conflict Arbitration Case: Taraxacum mongolicum (Weak Yin-tendency)\u003c/h2\u003e \u003cp\u003e① Basic Parameters and Conflict Determination\u003c/p\u003e \u003cp\u003eGroup attribute: Ordinary group (basic weights 40%/40%/20%, requiring arbitration);\u003c/p\u003e \u003cp\u003eCore indicator data (refer to [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]):\u003c/p\u003e \u003cp\u003eEnvironment: Annual sunshine 2800 h (Yang-tendency, intensity score 9), soil moisture 32% (neutral), pH 7.2 (neutral);\u003c/p\u003e \u003cp\u003eMetabolism: Flavonoids 3.5% (Yin-tendency, intensity score 6), photosynthetic rate 18 \u0026micro;molCO₂\u0026middot;m⁻\u0026sup2;\u0026middot;s⁻\u0026sup1; (neutral);\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConflict\u003c/strong\u003e \u003cp\u003everification: Environmental yang-tendency (9 points) vs. metabolic yin-tendency (6 points), difference\u0026thinsp;=\u0026thinsp;3 points \u0026rarr; trigger arbitration (weights 30%/60%/10%).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e② Calculation and Verification After Arbitration\u003c/p\u003e \u003cp\u003eMetabolic contribution: 6 \u0026times; (-1) \u0026times;60% =-3.60;\u003c/p\u003e \u003cp\u003eEnvironmental contribution: 9 \u0026times; (+\u0026thinsp;1) \u0026times;30% =+2.70;\u003c/p\u003e \u003cp\u003eGrowth contribution (maturity period 120 days, Yin-tendency, intensity score 5): 5 \u0026times; (-1) \u0026times;10% =-0.50;\u003c/p\u003e \u003cp\u003eTotal system score =-3.60\u0026thinsp;+\u0026thinsp;2.70\u0026thinsp;\u0026minus;\u0026thinsp;0.50\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1.40 \u0026rarr; calibrated to -4.20;\u003c/p\u003e \u003cp\u003eNature bias determination: Weak Yin-tendency (4\u0026ndash;5 points), consistent with the \"\u003cem\u003eTaraxacum mongolicum\u003c/em\u003e cold nature\" recorded in the \u003cem\u003ePharmacopoeia of the People\u0026rsquo;s Republic of China\u003c/em\u003e [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Analysis of System Validation Results\u003c/h2\u003e \u003cp\u003eThe reliability of the system was verified from three dimensions: \"consistency rate with traditional warm-cool nature, repeated detection error, and adaptability to extreme scenarios\". All data were cross-validated by \u0026ge;\u0026thinsp;2 studies, and outliers were processed by the Shapiro‒Wilk test (α\u0026thinsp;=\u0026thinsp;0.05).\u003c/p\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1 Verification of the Consistency Rate with Traditional Warm-Cool Nature (Refer to Authoritative Standards)\u003c/h2\u003e \u003cp\u003eThe span defined by \"maximum nature bias score - minimum nature bias score of the formula\" was used to define intensity, which was strongly positively correlated with the severity of syndrome types (r\u0026thinsp;=\u0026thinsp;0.87, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001):\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab14\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 15\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVerification Results of the Consistency Rate with Traditional Warm-Cool Nature\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsistency Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of Samples (species)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProportion (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTypical Cases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReason Analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFull consistency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.91 (97.11% for non-deep-sea)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eZingiber officinale\u003c/em\u003e (+\u0026thinsp;8.80\u0026rarr;warm nature), \u003cem\u003eFritillaria cirrhosa\u003c/em\u003e (-5.80\u0026rarr;mildly cold nature)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCore indicators are consistent with the material basis of traditional records, and rules are applied accurately [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePartial consistency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.89 (2.89% for non-deep-sea)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGreenhouse-grown Lactuca sativa (-4.70\u0026rarr;cool nature), Polar lichens (-7.20\u0026rarr;cold nature)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHabitat data deviation (greenhouse shading rate 60% \u0026lt; natural 70%), which can be corrected by supplementing details [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInconsistency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.21 (only deep-sea)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDeep-sea Laminaria japonica (-6.80\u0026rarr;neutral nature)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLack of a multifactor model for deep-sea environments, planning to supplement 20 deep-sea samples for optimization in 2025 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eKey conclusions: The consistency rate for nondeep-sea scenarios is 100%, the consistency rate for daily food materials is 98% (363/370 species), and the consistency rate for special groups of Chinese medicinal materials is 96% (110/115 species), which is significantly higher than the 65% of traditional qualitative evaluation [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2 Analysis of Repeated Detection Error (Defining Applicable Scope)\u003c/h2\u003e \u003cp\u003eTwenty core samples (15 daily\u0026thinsp;+\u0026thinsp;5 special groups) were selected, and nature bias scores were calculated crosswise through 3 groups of independent literature data. The coefficient of variation (CV) was used to evaluate errors, referring to the \"CV\u0026thinsp;\u0026le;\u0026thinsp;4.0%\" requirement in the \u003cem\u003eQuality Standards for Chinese Medicinal Materials\u003c/em\u003e [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab15\" class=\"InternalRef\"\u003e16\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab15\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 16\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistics on Repeated Detection Errors of Core Samples\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSample Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGroup 1 Nature Bias Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup 2 Nature Bias Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGroup 3 Nature Bias Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCV Value (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eData Sources\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNotes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaily food materials\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eZingiber officinale\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;8.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eExtremely small error, meeting standards\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaily food materials\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSolanum tuberosum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eLow nature bias components in neutral food materials, limited detection sensitivity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecial groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGanoderma lucidum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eStable correction rules for fungi\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecial groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCistanche deserticola\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;6.485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;6.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;6.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;6.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eReliable metabolic rules for parasitic plants\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eKey conclusions: The overall CV\u0026thinsp;=\u0026thinsp;3.2% (excluding neutral food materials), meeting standards; the error of special groups is lower (CV\u0026thinsp;\u0026le;\u0026thinsp;3.5%), verifying the stability of exclusive rules.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section3\"\u003e \u003ch2\u003e3.4.3 Adaptability Verification of Extreme Scenarios (Marking Sample Size and Plans)\u003c/h2\u003e \u003cp\u003eFor 3 types of nondeep-sea extreme habitats (20 samples), the adaptability rate reached 100% after applying exclusive correction rules. The results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab16\" class=\"InternalRef\"\u003e17\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab16\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 17\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAdaptability verification results of extreme scenarios\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtreme Scenario\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSample Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eApplication of Correction Rules\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSystem Calculation Result\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTraditional Warm-Cool Nature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eConsistency Status\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eData Sources\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSample Size Description\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-altitude (3200\u0026ndash;3500 m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFritillaria cirrhosa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYin-tendency score \u0026times;1.05; C3 photosynthetic threshold decreased by 20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate Yin-tendency \u0026minus;\u0026thinsp;5.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMildly cold nature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFully consistent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7 high-altitude species, planned to expand to \u0026ge;\u0026thinsp;15 species in 2025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSaline-alkaline land (pH 8.5\u0026ndash;10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlycyrrhiza uralensis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYang-tendency score \u0026times;0.75; Glycyrrhizic acid not corrected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeak Yang-tendency\u0026thinsp;+\u0026thinsp;4.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNeutral to slightly Yang-tendency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFully consistent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5 saline-alkaline species, planned to expand to \u0026ge;\u0026thinsp;10 species in 2025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtreme acidity (pH 3.5-4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCarex lasiocarpa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYin-tendency score \u0026times;1.1; Flavonoids \u0026times;1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate Yin-tendency \u0026minus;\u0026thinsp;6.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCold nature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFully consistent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8 extreme acidic species, planned to expand to \u0026ge;\u0026thinsp;10 species in 2025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eKey conclusions: Extreme scenarios can explain the differences in nature bias of medicinal materials from genuine producing areas (e.g., the difference in Yin-tendency scores between Panax notoginseng from Yunnan and Sichuan is 0.8), and the rules are promotable (e.g., the saline-alkaline land rules apply to Lycium barbarum from Ningxia and Glycyrrhiza uralensis from Xinjiang).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Summary of Key Findings\u003c/h2\u003e \u003cp\u003eBased on the verification of 485 core samples, the core laws and optimization directions of the system are summarized as follows:\u003c/p\u003e \u003cdiv id=\"Sec30\" class=\"Section3\"\u003e \u003ch2\u003e3.5.1 \"Function-Oriented\" Nature Bias Distribution\u003c/h2\u003e \u003cp\u003eDaily food materials: Neutral tendency accounts for 45.41% (168/370 species), no strong yin-tendency species, and only 6 strong yang-tendency species (for seasoning purposes), consistent with the \"mild dietary therapy\" positioning;\u003c/p\u003e \u003cp\u003eMedicinal plants: 77.78% of fungi have a neutral tendency (regulating medicinal properties), 75% of lichens have a moderate Yin tendency (clearing heat and anti-inflammation), and 85.71% of high-altitude plants have a moderate Yin tendency (nourishing Yin and moistening dryness), conforming to the \"efficacy attribute\" corresponding logic [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section3\"\u003e \u003ch2\u003e3.5.2 Sufficiently Verified Effectiveness in Non-Deep-Sea Scenarios\u003c/h2\u003e \u003cp\u003eWith a consistency rate of 100%, error of \u0026plusmn;\u0026thinsp;3.2%, and 100% adaptability to extreme scenarios, the framework of \"environment determines direction, metabolism dominates intensity\u0026thinsp;+\u0026thinsp;special correction\u0026thinsp;+\u0026thinsp;conflict arbitration\" is scientifically reliable and can be used as a standardized tool.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section3\"\u003e \u003ch2\u003e3.5.3 Core Thresholds Have Standardization Potential\u003c/h2\u003e \u003cp\u003e89% of the thresholds are consistent with traditional determination and compatible with international standards (e.g., soil moisture\u0026thinsp;\u0026le;\u0026thinsp;20% follows FAO [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], volatile oil\u0026thinsp;\u0026ge;\u0026thinsp;3% refers to GB/T11538 [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]), which can be promoted to dietary matching and genuine medicinal material determination (e.g., \u003cem\u003eFritillaria cirrhosa\u003c/em\u003e requires \"flavonoids\u0026thinsp;\u0026ge;\u0026thinsp;3.5% + Yin-tendency score\u0026thinsp;\u0026ge;\u0026thinsp;5.80\").\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec33\" class=\"Section3\"\u003e \u003ch2\u003e3.5.4 Clear Optimization Directions\u003c/h2\u003e \u003cp\u003eSample supplement: Expand deep-sea samples from 3 to \u0026ge;\u0026thinsp;23 species and high-altitude samples from 7 to \u0026ge;\u0026thinsp;15 species;\u003c/p\u003e \u003cp\u003eScenario coverage: Add rules for genetically modified plants and correction for compound scenarios such as \"high-altitude\u0026thinsp;+\u0026thinsp;saline-alkaline land\";\u003c/p\u003e \u003cp\u003eDetection optimization: Use UPLC to reduce the CV of neutral food materials to \u0026le;\u0026thinsp;4.0%.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eBased on 987 plant-derived samples, this study focused on 485 core samples to construct a three-dimensional \"environment-metabolism-growth\" system, integrating extreme correction and arbitration logic to achieve a breakthrough in nature bias evaluation from \"qualitative description\" to \"quantitative calculation\". The following discussion combines existing research and practical needs to elaborate on the innovation, comparative advantages, limitations, and future prospects of the system.\u003c/p\u003e \u003cdiv id=\"Sec35\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Innovation and Academic Value of the System\u003c/h2\u003e \u003cp\u003eThe three-dimensional system addresses three major pain points of traditional evaluation through a synergistic \"environment-metabolism-growth\" logic: ① The rule of \"environment determines direction and metabolism dominates intensity\" realizes the gradient quantification of \"warm/hot/cool/cold\" (e.g., \u003cem\u003eZingiber officinale\u003c/em\u003e with strong Yang-tendency and \u003cem\u003eZiziphus jujuba\u003c/em\u003e with weak Yang-tendency), solving the problem of indistinguishable intensity within the same attribute; ② Exclusive correction rules for special groups fill the gap in nature bias evaluation of fungi, high-altitude plants, and other groups, which were not covered by traditional systems; ③ Cross-scenario adaptation rules can explain the differences in nature bias of medicinal materials from genuine producing areas (e.g., the Yin-tendency score difference of Panax notoginseng between Yunnan and Sichuan is 0.8), providing a standardized basis for cross-regional application.\u003c/p\u003e \u003cp\u003eCompared with existing studies, the advantages of this system are prominent: compared with studies focusing only on metabolic components [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], the adaptability rate to non-deep-sea scenarios is increased by 30%; compared with studies focusing only on habitat factors [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], the consistency rate with traditional nature is increased by 16 percentage points. This system not only retains the core connotation of TCM \"Yin-Yang and four natures\" theory but also integrates modern plant physiology and statistical methods, realizing the organic combination of tradition and modernity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec36\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Comparative Analysis with Existing Studies\u003c/h2\u003e \u003cdiv id=\"Sec37\" class=\"Section3\"\u003e \u003ch2\u003e4.2.1 Difference from Traditional Qualitative Evaluation: From \"Implicit\" to \"Explicit Quantification\"\u003c/h2\u003e \u003cp\u003eTraditional evaluation relies on literature descriptions (e.g., Lycium barbarum as \"neutral/mildly warm\"), lacking intensity standards, ignoring habitat differences, and with a consistency rate of only 65% [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]; this system realizes four-level explicit intensity classification through objective indicators (Lycium barbarum with 5.0% polysaccharides \u0026rarr; extremely weak Yang-tendency\u0026thinsp;+\u0026thinsp;0.30 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]), quantifies habitat impacts (high-altitude Lycium barbarum\u0026thinsp;+\u0026thinsp;0.20 vs. plain\u0026thinsp;+\u0026thinsp;0.30 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]), and increases the consistency rate to 98%, making the evaluation from \"experience-dependent\" to \"data-driven\".\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec38\" class=\"Section3\"\u003e \u003ch2\u003e4.2.2 Difference from Existing Quantitative Studies: From \"Single Dimension\" to \"Comprehensive Framework\"\u003c/h2\u003e \u003cp\u003eMost existing studies focus on a single dimension of habitat or metabolism (e.g., Wang et al. 2023 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] only considers habitat, Chen et al. 2023 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] only focuses on growth), covering 0% of special groups and 1 type of extreme scenario; this system covers 23.7% of special groups and 3 types of extreme scenarios, supported by dual scenarios (daily dietary\u0026thinsp;+\u0026thinsp;medicinal) and verified by 485 multidimensional samples. The weights are derived from cross-group verification (CV\u0026thinsp;=\u0026thinsp;3.2%) rather than subjective assumptions, ensuring the scientificity and reliability of the system.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec39\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Limitations and Improvement Directions\u003c/h2\u003e \u003cdiv id=\"Sec40\" class=\"Section3\"\u003e \u003ch2\u003e4.3.1 Existing Limitations\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eSample and data limitations\u003c/strong\u003e \u003cp\u003eOnly 3 deep-sea samples are available (consistency rate 66.7% [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]) and 7 high-altitude samples (insufficient statistical significance); there are no independent measured data (e.g., dynamic changes in flavonoids in \u003cem\u003eFritillaria cirrhosa\u003c/em\u003e under altitude gradients), and some correction rules rely on a single study.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eScenario and method gaps\u003c/strong\u003e \u003cp\u003eGenetically modified plants (e.g., flavonoid-transgenic Lactuca sativa) are not covered; the CV of neutral food materials is 4.3% (close to the threshold), and detection sensitivity needs to be improved.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec41\" class=\"Section3\"\u003e \u003ch2\u003e4.3.2 Targeted improvements\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eSample expansion\u003c/strong\u003e \u003cp\u003eIn 2025, the State Oceanic Administration will cooperate to collect 20 deep-sea algae samples, establish a \"water depth-illumination-temperature\" multifactor model, and expand 15 high-altitude samples and 50 rare parasitic plants.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eExperimental verification\u003c/strong\u003e \u003cp\u003eConstruct a high-altitude simulation platform (2000\u0026ndash;4000 m) to measure metabolic data and conduct research on the impact of cooking processing (steaming and boiling for 10\u0026ndash;20 min) on volatile oil content.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eRule and method optimization\u003c/strong\u003e \u003cp\u003eEstablish the \"exogenous gene-nature bias correction\" logic for genetically modified plants (e.g., CHS gene \u0026rarr; flavonoid increment \u0026times;1.2); use UPLC to improve the detection sensitivity of neutral food materials, reducing CV to \u0026le;\u0026thinsp;4.0%.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec42\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Future Research Prospects\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eCorrelation between nature bias and efficacy\u003c/strong\u003e \u003cp\u003eEstablish a \"nature bias intensity-anti-inflammatory IC50\" model to formulate clinical dosage standards (e.g., strong Yin-tendency medicinal materials\u0026thinsp;\u0026le;\u0026thinsp;3 g/dose) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e];\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eIntelligent dietary therapy system\u003c/strong\u003e \u003cp\u003eDevelop a \"constitution-food material balance algorithm\" and launch a family dietary therapy app (e.g., recommending yang-tendency food materials with 3\u0026thinsp;~\u0026thinsp;5 points for yang-deficiency constitution);\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInternational herbal adaptation\u003c/strong\u003e \u003cp\u003eExpand samples to Ayurvedic and African herbal medicines, promoting the global standardization of nature bias quantification for traditional herbal medicines;\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eMolecular mechanism analysis\u003c/strong\u003e \u003cp\u003eUse transcriptomics to screen nature bias-related genes (e.g., DXS, F3H) and clarify the \"habitat-gene-metabolism-nature bias\" pathway [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eThe three-dimensional quantitative evaluation system for nature bias of plant-derived food materials constructed in this study takes the synergistic logic of \"environment-metabolism-growth\" as the core, clarifying the thresholds of core indicators in each dimension, correction rules for special groups, and arbitration logic. Verified by 485 core samples: the system covers more than 95% of common daily and medicinal plant groups, with a consistency rate of 98% for daily food materials with traditional warm-cool nature, 96% for special groups of Chinese medicinal materials after correction, a repeated detection error rate of \u0026plusmn;\u0026thinsp;3.2%, and a 100% adaptability rate to nondeep-sea extreme habitats. This system achieves a key breakthrough in nature bias from \"qualitative description\" to \"quantitative calculation\", which is not only suitable for balanced matching of nature bias in daily dietary therapy but also supports the standardized determination of nature bias of medicinal plants, laying a scientific rule foundation for subsequent extended studies such as the correlation between nature bias and efficacy of Chinese medicinal materials and scenario optimization for deep-sea/genetically modified plants.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNational Pharmacopoeia Commission of the People\u0026rsquo;s Republic of China (2020) \u003cem\u003ePharmacopoeia of the People\u0026rsquo;s Republic of China (2020 Edition, Volume I)\u003c/em\u003e. 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Full version available from the corresponding author\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang GF (2025), October 30 \u003cem\u003eSynergistic System for Three-Dimensional Evaluation and Triple-Component Formulation of Animal-Derived Ingredients: Practice and Rule Innovation in Traditional Chinese Medicine (TCM) Diet Therapy\u003c/em\u003e. Research Square. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21203/rs.3.rs-7960666/v1\u003c/span\u003e\u003cspan address=\"10.21203/rs.3.rs-7960666/v1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Traditional Chinese Medicine Nutrition, plant-derived food materials, nature bias quantification, three-dimensional evaluation system, environment-metabolism synergy, dietary matching","lastPublishedDoi":"10.21203/rs.3.rs-8381999/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8381999/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo address the long-standing problems of \"subjectivity, lack of standards for special groups, and poor cross-scenario adaptability\" in the traditional evaluation of nature bias (cool-warm property) of plant-derived food materials, this study aimed to construct a full-scenario quantitative tool applicable to both \"daily dietary\" and \"medicinal\" scenarios.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA three-dimensional framework integrating the \"environmental habits-metabolic characteristics-growth cycle\" was established. The core indicator thresholds of each dimension, exclusive correction rules for special groups (fungi, parasitic plants, lichens, high-altitude plants), and cross-dimensional arbitration logic were clarified. The feasibility of the system was verified using 485 core plant-derived samples, which were stratified and selected from a total sample library of 987 species (417 daily plant food materials\u0026thinsp;+\u0026thinsp;570 Chinese medicinal materials). The core samples covered 4 categories of daily food materials, 4 special groups of Chinese medicinal materials, and 3 types of extreme habitats based on \"biological group\u0026thinsp;+\u0026thinsp;ecotype\".\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe system covered more than 95% of common plant groups used in daily life and medicine. The consistency rate between daily food materials and traditional literature on \"warm-cool nature\" reached 98%, and the consistency rate of special groups of Chinese medicinal materials after correction reached 96% (with intensity error\u0026thinsp;\u0026le;\u0026thinsp;15% for partially consistent samples). The repeated detection error rate was \u0026plusmn;\u0026thinsp;3.2% (excluding neutral food materials). Core indicator thresholds were cross-validated by authoritative references such as FAO Guidelines and international journals, which were consistent with the \"environment-metabolism synergy\" logic. The adaptability rate to nondeep-sea extreme habitats (high altitude, saline‒alkali land, extreme acidity) was 100%, which could explain the differences in the natural bias of medicinal materials from genuine producing areas.\u003c/p\u003e","manuscriptTitle":"Establishment and Verification of a Three-Dimensional Quantitative Evaluation System for Nature Bias of Plant-Derived Food Materials","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-18 09:21:50","doi":"10.21203/rs.3.rs-8381999/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bd0e9911-4549-4588-bad7-4b0689176ce2","owner":[],"postedDate":"December 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":59791380,"name":"Nutrition \u0026 Dietetics"},{"id":59791381,"name":"Integrative \u0026 Complementary Medicine"},{"id":59791382,"name":"Drug Discovery, Design, \u0026 Development"},{"id":59791383,"name":"Botany"}],"tags":[],"updatedAt":"2025-12-18T09:21:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-18 09:21:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8381999","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8381999","identity":"rs-8381999","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}