Habitat suitability and quality division of Mentha haplocalyx
Bibliographic record
Abstract
In this study, ecological factors, occurrence records, the essential oil components content were used to predict the potential geographical distribution and quality division of Mentha haplocalyx in China based on the MaxEnt modeling and geographic information system(GIS). The AUC of ROC curve was above 0.950,indicating that the predictive results with the maximum model were highly precise. The results showed that the main environmental factors determining the potential distribution were annual average precipitation (the contribution rate, 45.87%), mean temperature of wettest quarter (11.92%), mean temperature of warmest quarter (7.84%), average monthly precipitation of May (6.80%), standard deviation of seasonal temperature variation (4.42%), mean temperature of the coldest quarter (3.47%) and altitude (2.92%). The environmental variables in the highly potential areas were determined as annual average precipitation around [530,1 465] mm, mean temperature of wettest quarter around [24.5,29] ℃, mean temperature of the warmest quarter around [25.5,29] ℃, average monthly precipitation of May around [67,133] mm, standard deviation of temperature seasonal change around [8 333,9 643], mean temperature of the coldest quarter around [1.7,8.3] ℃ and the altitude around [0,165] mm. The best quality distribution of M. haplocalyx was mainly located in Jiangsu, Anhui, Shandong, Zhejiang and Heilongjiang. The zoning results basically coincide with the actual situation. The quality division of M. haplocalyx can be used for providing a scientific basis for selection of artificial planting base and guidance of its production.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".