Projected Future Changes in Burn Probability in Canada’s Forests and Communities Under Different Climate Change Scenarios
Bibliographic record
Abstract
Vegetation inventories characterizing potential fuels represents critical information underpinning wildfire management and emergency response planning. Available fuels can be characterized in terms of burn probability, which describes the degree to which a set of biotic and abiotic conditions corresponds to known or simulated burned areas. Changes in future climate are expected to result in corresponding shifts in burn probability. In this study, existing burn probability models based on climate, vegetation, and topographic conditions were used as inputs with variables from four future climate scenarios to examine the spatiotemporal distribution of burn probability in the 21st century. Changes were calculated and analyzed for all pixels in forest-dominated ecozones in Canada and 160 forest-adjacent communities by comparing future projections to contemporary values of burn probability. By 2100, overall median projected burn probability increased by 17% across scenarios, ranging from 4 - 60% across individual ecozones. Burn probability likewise increased for the majority of forest-adjacent communities, although the magnitude of the increase was highly variable. The results of this study show the spatiotemporal distribution of changes in burn probability under future climate scenarios and provide valuable information for those interested in implementing mitigation techniques (e.g., prescribed burning, thinning, creation of defensible spaces or firebreaks) to reduce the impacts of future fires.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".