Simulating the impacts of future fire regimes and fire management strategies on vegetation and fuel dynamics in western Canada using a boreal fire effects model (BORFIRE).
Bibliographic record
Abstract
The long-term effects of different fire management strategies on boreal forest composition and fuels under future fire regimes were studied in four National Parks in western Canada using a boreal fire effects model. Longer fire cycles caused by increased fire suppression resulted in greater fuel accumulation and a general increase in fire behaviour. Long fire cycles favoured Picea glauca, Picea mariana and Abies balsamea, three highly flammable tree species. Shorter fire cycles established by prescribed burning decreased fuel load and fire behaviour. Pinus banksiana and Picea mariana stands survived under shorter fire cycles, but Populus tremuloides and Betula papyrifera were greatly favoured, mostly at the expense of declining Picea glauca. A shift in forest composition towards low flammability species such as Populus tremuloides and Betula papyrifera would cause a general decrease in landscape fire danger.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".