Planning Ginkgo biloba future fruit production areas under climate change: Application of a combinatorial modeling approach
Bibliographic record
Abstract
Ginkgo biloba forests are widely studied for their fruits’ high medicinal, edible, and economic values with an increasing global demand for its fruits supply. The impact of climate change on the habitat suitability of this species has been assessed in previous studies. This study was to address the climate change impact on both habitat suitability and fruit yield. We used fruit grain weight to represent fruit productivity and quality of Ginkgo fruit forests and related it to climate variables to develop a climate response function (Method I). Meanwhile, we also built a Maxent habitat suitability model and associated it with fruit grain weight (Method II). Results showed that Method I provided much higher prediction accuracy (R2 = 0.68) than Method II (R2 = 0.39). We combined Method I with the species habitat model to predict suitable production areas of Ginkgo fruit to achieve high fruit production within suitable habitats for the contemporary and future periods. We found that Ginkgo fruit grain weight was mainly affected by mean annual average temperature (MAT) and mean annual precipitation (MAP), while the degree-days below 0 ℃ (DD < 0) was the main climate factor limiting the distribution of Ginkgo fruit forests suitable habitat. The combined model predictions showed that the suitable production areas of Ginkgo fruit forests are expected to decrease and move northeastward in the future under the Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios. Our predictions can be used to maximize Ginkgo fruit quality production while minimizing the risk of low survival in the planning of Ginkgo fruit forest production areas.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".