Role of vegetation and weather on fire behavior in the Canadian mixedwood boreal forest using two fire behavior prediction systems
Bibliographic record
Abstract
Spring and summer simulations were carried out using the Canadian Fire Behavior Prediction (FBP) and U.S. BEHAVE systems to study the role of vegetation and weather on fire behavior in the mixedwood boreal forest. Stands at Lake Duparquet (Quebec, Canada) were characterized as being deciduous, mixed-deciduous, mixed-coniferous, or coniferous, according to their conifer basal area percentage. Sampled fuel loads (litter, duff, woody debris, herbs, and shrubs) and local weather conditions (three different fire-risk classes) were used as inputs in the simulation. The predicted fire behavior variables were rate of spread (ROS), head fire intensity (HFI), and area burned. Results from ANOVA testing showed that both weather and vegetation are not always significant, and the two prediction systems qualitatively attribute the explained variance to these factors differently. The FBP System selects the weather factor as the most important factor for all fire behavior variables, whereas BEHAVE selects the vegetation factor. However, three research burns located in Ontario revealed that BEHAVE was not well adapted to the mixedwood boreal region, whereas FBP predictions were quantitatively close to observed prescribed values. Extreme fire weather is confirmed as producing large and intense fires, but differences in fire behavior among stand types exist across the full range of fire weather. Implications of climate change, vegetation, and seasonal effects on fire behavior and the forest mosaic are discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".