Changes in escape fire occurrence rate in Canada's boreal forest under climate change
Bibliographic record
Abstract
Recent studies have shown that fire occurrence (from both human and lightning causes) is expected to increase across the boreal forest in Canada (and in many other regions of the world) with the fire weather expected to accompany climatic change in the 21st Century. Knowing total number of fires on the landscape is important for fire managers as part of their determination of load on the suppression organization’s resources; however in terms of impact on the landscape (e.g., area burned or loss of values) it is that very small number of fires that escape initial attack that have the greatest impact. In this study, which covers the forest area of Canada, models of the probability of a fire escaping initial attack are developed based on the outputs of the Canadian FWI System, general fire cause and fire load. Using these models with outputs from recent General Circulation Model scenarios from the Hadley and Canadian Climate Centre were used and indicated an overall increase in expected fire escapes across the forested region of Canada. These increases are spatially quite variable however, due to the interaction between increased temperature and increased precipitation. Results between these two GCM scenarios do show some variation in parts of the country however, leading to some uncertainty in the absolute level of predicted change. The basic assumption of this analysis is that Canadian fire management agency efforts, in terms of response time and suppression resource levels, remain constant over time.
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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 | 0.002 |
| 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".