Fire regime zonation under current and future climate over eastern Canada
Why this work is in the frame
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Bibliographic record
Abstract
Fire is a major disturbance in Canadian forests. Along with fuel and ignition characteristics, climatic conditions are seen as one of the main drivers of fire regimes. Projected changes in climate are expected to significantly influence fire regimes in Canada. As fire regime greatly shapes large-scale patterns in biodiversity, carbon, and vegetation, as well as forest and fire management strategies, it becomes necessary to define regions where current and future fire regimes are homogeneous. Random Forests (RF) modeling was used to relate fire regime attributes prevailing between 1961 and 1990 in eastern Canada with climatic/fire-weather and environmental variables. Using climatic normals outputs from the Canadian Regional Climate Model (CRCM), we delineated current (1961-1990) and future (2011-2040, 2040-2070, 2071 2100) homogeneous fire regime (HFR) zones. Heterogeneous response of fire regime to climate changes is projected for eastern Canada with some areas (e.g., western Quebec) experiencing very small alterations while others (e.g., southeastern Ontario) are facing great shifts. Overall, models predicted a 2.2- and 2.4-fold increase in the number of fires and the annual area burned respectively mostly as a result of an increase in extreme fire-weather normals and mean drought code. As extreme fire danger would occur later in the fire season on average, the fire season would shift slightly later (5-20 days) in the summer for much of the study area while remaining relatively stable elsewhere. Although fire regime values would change significantly over time, most zone boundaries would remain relatively stable. The information resulting from HFR zonations is clearly of interest for forest and fire management agencies as it reveals zones with peculiar fire regimes that would have been hidden otherwise using predefined administrative or ecological stratifications.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 it