A methodology for investigating trends in changes in the timing of the fire season with applications to lightning-caused forest fires in Alberta and Ontario, Canada
Bibliographic record
Abstract
Lightning-caused fires account for approximately 45% of ignitions and 80% of area burned by forest fires in Canada. Investigating the seasonality of these fires and the extent to which it may be changing over time is of interest to both fire managers and researchers. In this project, we develop flexible models for describing the temporal variation in the risk of lightning-caused fires. Generalized additive models are first used to obtain smooth estimates of fire risk by Julian day for each year. Inverse calculations are then employed to obtain point and interval estimates of the start and end of the fire season annually; these are defined by the crossing of fire risk thresholds. Finally, permutation-based methods are used to test for significant linear trends in the start and end of the fire season. This methodology is applied to historical forest fire records in Alberta, Canada, and the western and eastern subregions of Ontario, Canada. Our results suggest significant changes to both the start and end of the fire season in Alberta and a significant change to the end of the fire season in western and eastern Ontario.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".