The effect of aerial suppression on the containment time of Australian wildfires estimated by fire management personnel
Bibliographic record
Abstract
The addition of aerial firefighting resources to wildfire suppression operations does not always result in faster fire containment. In this paper, containment times of fires with aerial suppression are compared with estimated containment times for the same fires without aerial suppression. Senior firefighting personnel who had worked on each fire estimated whether fires could have been contained within a time class if aircraft were not available. Data from 251 wildfires were analysed based on four fire-containment time classes: ≤2, 2–4, 4–8 and 8–24 h from the start of initial attack. Aircraft were perceived to reduce time to containment when firefighting conditions were more challenging owing to fuel hazard rating, weather conditions, slope, resource response times and area burning at initial attack. Comparisons of containment time with and without aircraft can be used to develop operational tools to help dispatchers decide when aircraft should be deployed to newly detected fires.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".