Assessing global pine wilt disease risk based on ensemble species distribution models
Bibliographic record
Abstract
Pine wilt disease (PWD), caused by the invasive pine wood nematode, is a major threat to global pine forests. This study utilized global PWD occurrence data alongside climatic, soil, and topographic variables to develop an ensemble of species distribution models. Using this ensemble model, we identified key factors influencing PWD and assessed the risk for current conditions and future periods (2041–2060 and 2071–2090) under three climate scenarios (SSP126, SSP370, and SSP585). The results indicate that key factors include the average temperature during the hottest quarter, clay content in soil, and precipitation during the hottest quarter, total annual precipitation, and precipitation during the coldest quarter. Currently, southern and northeastern China, central-southern Europe, and Southeast Asia are at high risks. With future climate changes, potential risk areas are expected to expand to higher latitudes, affecting regions like Hokkaido, Canada, and Northern Europe, especially under the SSP585 scenario. This study offers essential insights for global PWD prevention and forests resource conservation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".