Landscape composition influences local pattern of fire size in the eastern Canadian boreal forest: role of weather and landscape mosaic on fire size distribution in mixedwood boreal forest using the Prescribed Fire Analysis System
Bibliographic record
Abstract
Wildfire simulations were carried out using the Prescribed Fire Analysis System (PFAS) to study the effect of landscape composition on fire sizes in eastern Canadian boreal forests. We used the Lake Duparquet forest as reference, plus 13 forest mosaic scenarios whose compositions reflected lengths of fire cycle. Three fire weather risks based on duff moisture were used. We performed 100 simulations per risk and mosaic, with topography and hydrology set constant for the reference. Results showed that both weather and landscape composition significantly influenced fire sizes. Weather related to fire propagation explained almost 79% of the variance, while landscape composition and weather conditions for ignition explained ∼14 and 2% respectively. In terms of landscape, burned area increased with increasing presence of shade-tolerant species, which are related to long fire cycles. Comparisons among the distributions of cumulated area burned from scenarios plus those from the Société de Protection des Forêts contre le Feu database archives showed that PFAS simulated realistic fire sizes using the 80–100% class of probable fire extent. Future analyses would best be performed on a larger region as the limited size of the study area could not capture fires larger than 11 000 ha, which represent 3% of fires but 65% of the total area burned at the provincial scale.
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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 | 0.000 | 0.000 |
| 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".