Assessment of Suitable Habitats and Quality of <i>Siraitia grosvenorii</i> in China
Bibliographic record
Abstract
ABSTRACT Climate change impacts the habitats of medicinal plants and potentially affects the quality of herbal medicines. Siraitia grosvenorii , a crucial medicinal and edible traditional Chinese material endemic to China, requires more research on climate adaptation. This study employed Maxent and ArcGIS software to predict suitable habitats for S. grosvenorii across various periods in China. High‐Performance Liquid Chromatography and enzyme‐linked immunosorbent assay measured mogrosides V (MV) content, total flavonoid content (TFC), and total phenolic acid (TPA) in samples from different suitable habitats. Additionally, the in vitro antioxidant potency composite index (APCI) of various samples was compared. The results indicate that precipitation and temperature emerged as significant factors influencing the distribution of S. grosvenorii , with precipitation of warmest quarter (Bio_18), temperature seasonality (Bio_4), and precipitation of wettest quarter (Bio_16) identified as the key factors. Currently, suitable habitats for S. grosvenorii are primarily located south of the Yangtze River, especially in Guangxi and Guangdong Provinces. Future projections indicate a northward expansion of suitable habitats. MV content was significantly higher in samples from high‐ and medium‐suitability habitats compared to those from low‐suitability habitats. Conversely, TFC, TPA, and APCI values were higher in low‐suitability habitats. These findings offer valuable insights for identifying optimal cultivation areas and assessing the quality of S. grosvenorii resources in China.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".