Modelling decisions concerning the dispatch of airtankers for initial attack on forest fires in Ontario, Canada
Bibliographic record
Abstract
Airtankers are commonly used for initial attack (IA) to reduce the likelihood of wildland fires escaping containment efforts. We examined IA airtanker dispatch decisions for forest fires in Ontario, Canada, through an analysis of historical fire records from 2001 to 2019. A hurdle modelling approach that predicts the probability of airtanker(s) being dispatched and then the number of airtankers sent was used. Two different hurdle models were considered depending on the timing of the information available to the decision maker: a “fire report model” based upon the information available when a fire is first reported, and an “initial attack model” using information available at the time IA action began. Both models indicated that the most influential covariates for airtanker dispatch are the fire weather index, fuel volatility, observed fire rate of spread, fire size at IA, and cause of ignition. When evaluating the predictive ability of the models on a validation data set, the “initial attack model” performed better than the “fire report model”. Our models generally perform well when predicting none, one, or two airtankers on IA, but they generally underpredict when more than two airtankers are dispatched, which suggests risk-averse decision-making in fire management resource dispatching.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".