The extent of drought determines daily area burned in Canadian forests
Bibliographic record
Abstract
Abstract Annual or seasonal wildfire burned area has been frequently and successfully estimated by models in fire research; daily area burned (DAB) over a region, however, has never been effectively modeled due to its high variability. This study identified for the first time a strong relationship between DAB and the spatial extent of fire-conducive weather conditions, especially measured by fuel aridity, in Canadian forests. Observations between 2001 and 2023 used to develop the DAB prediction models showed about 126 active burning days per year and an average DAB of 20 788.93 ha nationally, with about two-thirds of these active burning days occurring in summer. The central boreal forests in Canada experienced both more active burning days and higher DAB, while the more extreme DAB occurred in the eastern region. Of the predicted DAB in Canadian forests between 1940 and 2023 using the developed DAB models, 62 d showed a significant increasing trend, averaging about 60.14 ha per year nationally. Such increases were found mainly in the central region, in summer, and between 2000 and 2023. Daily fire activity has also become more concentrated within the fire season, particularly in the eastern region. From 1940 to 2023, the lengths of the periods covering 50% and 90% of annual area burned decreased by 0.12 d and 0.25 d per year, respectively, across the country. Concurrently, extreme DAB events have become more extreme. Over the 84 year period, summer maximum DAB increased by 133.61 ha, number of extreme burning days (days with DAB exceeding the 84 year mean by one standard deviation) increased by 0.36 d, and proportion of area burned within these extreme days increased by 0.33% annually in Canadian forests.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".