Prior wildfires influence burn severity of subsequent large fires
Bibliographic record
Abstract
With longer and more severe fire seasons predicted, the incidence and extent of fires are expected to increase in western North America. As more area is burned, past wildfires may influence the spread and burn severity of subsequent fires, with implications for ecosystem resilience and fire management. We examined how previous burn severity, topography, vegetation, and weather influenced burn severity on four wildfires, two in Idaho, one in Washington, and one in British Columbia. These were large fire events, together burning 330 000 ha and cost $165 million USD in fire suppression expenditures. Collectively, these four study fires reburned over 50 000 ha previously burned between 1984 and 2006. We used sequential autoregression to analyze how past fires, topography, vegetation, and weather influenced burn severity. We found that areas burned in the last three decades, at any severity, had significantly lower severity in the subsequent fire. Final models included maximum temperature, vegetation cover type, slope, and elevation as common predictors. Across all study fires and burning conditions within them, burn severity was reduced in previously burned areas, suggesting that burned landscapes mitigate subsequent fire effects even with the extreme fire weather under which these fires burned.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".