Defining fire spread event days for fire-growth modelling
Bibliographic record
Abstract
Forest fire managers have long understood that most of a fire’s growth typically occurs on a small number of days when burning conditions are conducive for spread. Fires either grow very slowly at low intensity or burn considerable area in a ‘run’. A simple classification of days into ‘spread events’ and ‘non-spread events’ can greatly improve estimates of area burned. Studies with fire-growth models suggest that the Canadian Forest Fire Behaviour Prediction System (FBP System) seems to predict growth well during high-intensity ‘spread events’ but tends to overpredict rate of spread for non-spread events. In this study, we provide an objective weather-based definition of ‘spread events’, making it possible to assess the probability of having a spread event on any particular day. We demonstrate the benefit of incorporating this ‘spread event’ day concept into a fire-growth model based on the Canadian FBP System.
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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) | 0.000 | 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".