Wildfire containment probability is not affected by eastern spruce budworm defoliation in Ontario, Canada
Bibliographic record
Abstract
Background Stand-replacing wildfires and eastern spruce budworm outbreaks (Choristoneura fumiferana; SBW) are important disturbances in the boreal forest. SBW defoliation can affect fire behaviour by altering fuel loads and connectivity, thereby promoting the transition of low-activity surface fires into crown fires. However, little is known about how these altered fuels impact the effectiveness of fire suppression. Aims To assess key drivers of initial attack (IA) success in Ontario’s boreal forest and determine if incorporating SBW defoliation data improves predictive models. Methods We developed random forest models of fire containment using established predictors including fire weather, fire size at IA and region. We then evaluated if the inclusion of time since SBW defoliation improved model performance. Key results Fire size at IA was the most influential variable for determining whether a fire escaped containment. Contrary to our hypothesis, we did not find evidence that SBW defoliation greatly improved model performance. Conclusions and implications The size of the fire at IA was the most important variable in determining successful containment. Although budworm defoliation has been shown to affect other aspects of fire hazard, we were unable to identify an influence on IA success. Future work could benefit from focused investigation into how historical SBW defoliation affects fire behaviour.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.002 | 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".