Extremely large fires shape fire severity patterns across the diverse forests of British Columbia, Canada
Bibliographic record
Abstract
Abstract Warming and drying conditions are driving increases in wildfire size and annual area burned across the forests of British Columbia, Canada. The impact of increasing fire activity on these forests remains unclear as examination of concurrent changes to fire severity is lacking. Here, we assess how fire severity patterns change with the amplification of wildfire size across the bioregions of British Columbia using fire severity mapping from 1986 to 2021. First, we examine trends in extremely large fires (i.e., largest 5% of fires) and their influence on annual area burned; then we examine scaling relationships between wildfire size and fire severity to determine if extremely large fires are more severe than smaller fires. Extremely large fires explained much of the variation in annual area burned and accounted for a large proportion of cumulative area burned (33%–71%) across the study area. Consequently, shifts in the size of extremely large fires, which increased by an order of magnitude over our study period, have driven a substantial increase in annual area burned. Scaling relationships revealed that bigger wildfires consisted of larger and more homogenous patches of high‐severity fire than smaller fires, resulting in a greater proportional contribution of high‐severity fire to fire extent. Patterns in scaling relationships were qualitatively similar for all bioregions, indicating shifts in fire regimes are widespread across the province. Our results demonstrate that recent increases in the extremes of wildfire size across the forests of British Columbia have driven a sharp increase in area burned, which was associated with a disproportionate increase in the size and extent of patches of high‐severity fire.
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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 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".