Distribution and in situ conservation of a relic Chinese oil woody species <i>Xanthoceras sorbifolium</i> (yellowhorn)
Bibliographic record
Abstract
To understand the contemporary and anticipated future (future 30–50 years) distribution of Chinese wild yellowhorn (Xanthoceras sorbifolium Bunge) and to improve the species’ in situ conservation strategy within the network of China’s National Nature Reserves (NNR), we used BiodiversityR to predict the species’ distribution utilizing the “always-suitable” map concept. We then delineated the always-suitable distributions with the existing NNRs to identify potential conservation areas using an approach that concurrently considered spatial distribution, gap analysis, the role of climate change, and economic analyses. Seven bioclimatic variable predictors and 12 environmental niche modelling submodels successfully contributed to the final model assembly (AUC = 0.916, κ = 0.398). The species range delineation indicated that 71 of the 427 NNRs were included in the always-suitable area, accounting for 26 007 km2 (1.58%) of the species total distribution. This mapping endeavour highlighted the negative impact of climate change with a projected 15%–20% habitat decline and expected species’ distribution centers shifting from the country’s northwest to the southeast. Our results predict the continuous deterioration of X. sorbifolium because of its existing utilization as an oil source and its increased bioenergy potential. The adoption of a flexible management strategy embracing acceptable trade-offs between conservation and utilization within China’s NNRs could effectively alleviate the expected species decline.
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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 | 0.001 | 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 | 0.001 | 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 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".