Predicting suitable habitats of ginkgo biloba L. fruit forests in China
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 0.001 |
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 it