Modelling initial attack success on forest fires suppressed by air attack in the province of Ontario, Canada
Bibliographic record
Abstract
Airtankers are often used on initial attack (IA) to suppress unwanted wildland fires quickly and contain them before they grow large. Skimmer airtankers are commonly used in the province of Ontario owing to its abundance of waterbodies. We examined the influence of airtanker use on IA success on fires actioned by air attack in Ontario using historical fire records and developed three statistical models to estimate the probability of IA success using information available at three different times during the fire response process. These models include information available to the fire management agency at the time the fire was reported, when IA began and during the IA suppression operations. Our findings indicate that the situational information about a fire obtained during IA provides better estimates of the probability of IA success, as demonstrated by increases in the predictive accuracy and area under the receiver operating characteristic curve compared with a model that is based only on information available at the time a fire is reported. Our results can inform pre-suppression planning and suppression resource allocation decision-making, particularly on days during which many new fires are expected to be reported.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".