Evaluating a model for predicting active crown fire rate of spread using wildfire observations
Bibliographic record
Abstract
We evaluated the predictive capacity of a rate of spread model for active crown fires (M.G. Cruz, M.E. Alexander, and R.H. Wakimoto. 2005. Can. J. For. Res. 35: 1626–1639) using a relatively large (n = 57) independent data set originating from wildfire observations undertaken in Canada and the United States. The assembled wildfire data were characterized by more severe burning conditions and fire behavior in terms of rate of spread and the degree of crowning activity than the data set used to parameterize the crown fire rate of spread model. The statistics used to evaluate model adequacy showed good fit and a level of uncertainty considered acceptable for a wide variety of fire management and fire research applications. The crown fire rate of spread model predicted 42% of the data with an error lower then ±25%. Mean absolute percent errors of 51% and 60% were obtained for Canadian and American wildfires, respectively. The characteristics of the data set did not allow us to determine where model performance was weaker and consequently identify its shortcomings and areas of future improvement. The level of uncertainty observed suggests that the model can be readily utilized in support of operational fire management decision making and for simulations in fire research studies.
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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 | 0.007 | 0.012 |
| 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 | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".