Empirical models of forest fire initial attack success probabilities: the effects of fuels, anthropogenic linear features, fire weather, and management
Bibliographic record
Abstract
Canadian fire managers seek to contain fires below some target size (here 3 ha) by initial attack (IA). Suppression failures occur when fire size at IA exceeds this target (a response failure) or if an initially small fire cannot be contained below it (a containment failure). We examined the effects of cause, season, forest fuels, anthropogenic linear features, weather, and fire management (response time, size at IA) on the probability of these two types of suppression failures, using multiple logistic regression on 1196 fires that occurred within the boreal mixedwood forest of northeastern Alberta during 1995–2002. The frequencies of containment (7%) and response failures (10%) were similar, but the latter accounted for 85% of the area burned. Response failure probability was greater for fires caused by lightning than those caused by humans and increased with longer response times, local abundance of black spruce in summer, and pine fuel under severe fire weather. We found no effect of linear features or other fuel types. Containment failure probability was related to size at IA and fire weather conditions. Our models suggest that a reduction in area burned might be possible if additional fire-specific factors affecting response failure probability could be incorporated into operational decisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".