Factors that affect the timing of the dispatch of initial attack resources to forest fires in northeastern Ontario, Canada
Bibliographic record
Abstract
The success of forest fire initial attack systems is believed to be affected by many factors including the initial attack response time. Despite the fact that fire managers typically strive to dispatch initial attack resources to most fires soon after they are reported in order to minimise their response time, they may not always be able to do so as the timing of the initial attack dispatch can be influenced by many factors. We examine the effects of the following factors on the initial attack dispatch process: the daily fire load (the number of fires reported each day), the time of day the fire was reported, fire weather conditions, fire cause and the month of the fire season, on the probability that initial attack resources are dispatched on the day that a fire is reported. Logistic regression methods are used to analyse a dataset composed of 4532 forest fires that were reported in our study area in a portion of northeastern region of Ontario, Canada, during 1963–2012 fire seasons. Our results indicate that the time of day a fire is reported, the total number of fires reported on that day and the Initial Spread Index are key factors that influence the timing of the initial attack response in our study area.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".