Enhanced prediction of extreme fire weather conditions in spring using the Hot-Dry-Windy Index in Alberta, Canada
Bibliographic record
Abstract
Background Fire weather indices forecast fire behaviour and provide valuable information for wildland fire prevention, preparedness, and suppression. However, these indices do not directly account for atmospheric conditions aloft. The province of Alberta, Canada has experienced extreme fire weather conditions during spring for decades, leading to the continued occurrence of disastrous wildland fires. Aims We examined the Hot-Dry-Windy Index (HDWI) and spread days over the first 4 days of 80 large wildland fires that started in May 1990–2019 in Alberta. Methods HDWI values were calculated using ERA5 reanalysis data from the 1000, 975 and 950 hPa levels. Differences between HDWI distributions on spread days and non-spread days were examined using permutation tests. Initial Spread Index was also examined as it is considered an important Fire Weather Index System value for wildland fire spread during spring in Alberta. Key results Higher median HDWI values were observed on spread days than non-spread days, where median Initial Spread Index values showed little to no difference. Conclusions This analysis suggests that HDWI can contribute to the prediction of significant spring wildland fire spread in Alberta. Implications Forecasted HDWI and HDWI climatologies may provide additional decision support for wildland fire management agencies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".