A comparative analysis of wildfire initial attack containment objectives and modelling strategies in Ontario, Canada
Bibliographic record
Abstract
Background Fire management agencies use the proportion of fires classified as initial attack (IA) success as a suppression performance metric, making IA success a common indicator of suppression effectiveness in research. The criteria and definition for IA success vary based on operational objectives, making comparisons across studies difficult. Aims To examine the sensitivity of different time and size-based IA success definitions on model predictive accuracy and compare different modelling approaches. Methods Using 30 years of historical fire report data from Ontario, Canada (n = 26,171), we developed logistic regression models, bagged classification trees and random forest models to predict IA success for eight different definitions. Model predictive accuracy, sensitivity and specificity were assessed on an independent validation dataset. Key results The eight definitions classified between 79 and 98% of fires as IA successes. There was no clear pattern between model strength across prediction metrics and IA success definition. Logistic regression generally outperformed machine learning methods in classifying IA escapes. Conclusions and implications The definition of IA success does not greatly impact model performance across the modelling techniques used. Models of IA success and suppression-system performance metrics should be set with specific research or operational objectives in mind.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".