Fire regime changes in Canada: an update
Bibliographic record
Abstract
Given the recent rise in extreme fires, we present an update to a previous Canadian wildfire trend analysis (1959–2015) with nine additional years of data (2016–2024), an improved area burned dataset, a refined trend analysis method, and a greater geographical coverage of the country. Overall, the big-picture trends remain consistent: annual area burned, the annual number and size of large fires are still increasing, while fires of all sizes continue to decline. The most significant and consistent changes include greater fire activity in the Cordillera and Plains ecozones in the west, while increasing or flat trends in annual area burned are now evident in all ecozones. Very large fires (≥20 000 ha) are getting larger and account for a greater proportion of area burned. In contrast to the preceding analysis, human-caused fires, which were previously detected as decreasing in annual area burned, have now been increasing with high confidence since the mid-2000s.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".