Predicting suitable habitats of ginkgo biloba L. fruit forests in China
Bibliographic record
Abstract
Ginkgo fruit can be used for food and medicine with high economic values. It is of great importance to ensure its sustainable production and genetic resource protection under climate change. In this study, niche models built with climate and soil variables, respectively, were used to assess the impact of climate change on its potential suitable habitat. The model performance was excellent for the climate model (AUC = 0.92) and good for the soil model (AUC = 0.84). Three climate variables (degree-days below zero, mean coldest month temperature, and mean annual precipitation) and two soil variables (subsoil cation exchange capacity and topsoil cation exchange capacity) were the main factors determining the distribution of ginkgo fruit forests. The level of predicted habitat suitability was consistent with the differences observed in fruit traits, suggesting that our model predictions make biological and economic sense. The high- and medium-suitable habitats of this species would decrease in future climates under both the Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 climate change scenarios. This study contributed to a better understanding of the impact of climate change on ginkgo fruit forests and provided potential geographical areas for the cultivation and conservation of this species.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".