Quantile‐Based Fire Weather Index Better Informs Detection and Variability of Wildfire Risks in China
Bibliographic record
Abstract
Abstract The Fire Weather Index (FWI) system, developed in Canada, has now been widely used to monitor and forecast wildfires worldwide. However, it shows a marked mismatch to wildfire occurrences and burned areas when applied to the domain of China, thus hindering the understanding on the response of fire risks to climate change in the populated area. Here, we designed quantile‐based FWI (QFWI) that proves to be more accurate in indicating wildfire occurrences across the country. Based on the correspondence of fire incidence and burned areas to QFWI, we proposed a classification scheme indicative of low‐, medium‐, and high‐fire dangers in China, and further examined the historical variability of wildfire dangers accordingly. The results show that China's wildfire risks did not increase monotonically with warming, but exhibited an inter‐decadal variability modulated synergistically by changes in multiple climate variables. From 1961 to 1990, the high‐risk days decreased at −0.98 ± 0.71 days yr −1 primarily driven by the slowdown of daily maximum wind speed (WS max ). After 1990, broad swathes of the country experienced significant increases (0.64 ± 0.54 days yr −1 ) in high‐risk days, particularly in southwest China, contributed mainly by warming of daily maximum air temperature ( T max ) and drying of daily minimum relative humidity (RH min ). The increasingly favorable weather conditions in combination with continuous fuel accumulation from massive afforestation efforts alarmingly signals hightened wildfire risks into future decades, even in humid southern China.
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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) | 0.000 | 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) | 0.001 | 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".