Contextualizing recent increases in Canadian boreal wildfire activity: decadal burn rates still within historical variability of the two past centuries
Bibliographic record
Abstract
With approximately 15 million ha burned, the 2023 wildfire season in Canada was exceptional. However, it remains unclear whether such recent increases in burned areas exceed the range of variability observed over past centuries. The objective of this study was to leverage available dendrochronological reconstructions of decadal burn rates to contextualize their recent increase within their historical variability over the past two centuries. We compared decadal burn rate reconstructions based on dendrochronological data (1800–2023) for five large eastern and western Canadian boreal forest zones to those of recent decades up to 2023. The area burned in 2023 ranged from 0.76% to 32.5% among the five zones, which is unprecedented compared to the proportion recorded since 1972 for four of the five zones analyzed. In contrast, the burn rates of the decade ending in 2023 (i.e., 2014–2023) generally remained within the natural range of variability of the last two centuries. However, burn rates in two zones were close to the highest decadal burn rates observed since the 1800s and exceeded historical variability in one zone in western Canada. We discuss the historical and current trends in burn rates, their drivers and implications.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".