Lightning‐caused forest fire risk in Northwestern Ontario, Canada, is increasing and associated with anomalies in fire weather
Bibliographic record
Abstract
Results from studies of climate model scenarios suggest that forest fire ignitions will increase in Canada in the future because of climate change. Yet, there have been few studies that monitor long‐term trends in Canadian historical fire records. Although there are seasonal trends to historically reported fires within a fire season, there are also periods of zero‐heavy behaviour as well as periods during which more fires are reported than usual. We develop a flexible mixture‐modelling framework that permits the joint assessment of temporal trends in these dominant characteristics in terms of fire risk, defined as the daily probability that one or more fires are reported. The statistical power of such trend tests are also evaluated. We identify statistically significant increases in lightning‐caused fire risk between 1963 and 2009 in the boreal forest regions of the Rainy River and Lake of the Woods ecoregions in Northwestern Ontario, Canada. These observed changes in lightning‐caused fire risk were found to be associated with temperature and fire danger rating index anomalies. If such trends continue into the future, the duration of elevated periods of lightning‐caused forest fire risk is forecasted to increase by over 50% by the middle of this century. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".