Predicting dynamics of wildfire regimes in Yunnan, China
Bibliographic record
Abstract
Abstract In recent years, the rise in global warming has significantly increased forest fires, affecting the environment and economy. Predicting forest fire dynamics under climate change is now a crucial research field. To address this need, this study focuses on the impact of climate change on forest fires, with a particular focus on the fire dynamics in Yunnan Province. This study utilizes the RegCM regional climate model and the Canadian Fire Weather Index (FWI) to simulate and analyze forest fire dynamics in Yunnan Province from 2019 to 2033 under three climate scenarios: RCP2.6, RCP4.5, and RCP8.5. Findings indicate climate change will increase temperatures, alter humidity and wind speed, and reduce precipitation in Yunnan, extending the fire danger period, especially under RCP8.5 scenarios. The FWI values rise across Yunnan, particularly in the west under RCP2.6 and RCP8.5. The study concludes that future carbon emissions correlate with these changes, leading to more frequent, longer, and severe forest fires. This research is vital for managing and preventing forest fires in Yunnan, a region prone to such disasters.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".