Effects of wind velocity on predictions of wildland fire rate of spread models: A comparative assessment using surface fuel fire tests
Bibliographic record
Abstract
In this work, a collection of ten wildland fire rate of spread prediction models that take into account the effects of wind are reviewed and tested against 166 individual laboratory fire tests, available in the open literature. The investigated models include the well-known semi-empirical models of Rothermel, Wilson and Catchpole et al., the empirical models of Rossa and Fernandes, developed using laboratory fire tests and the empirical models of Burrows et al., Anderson et al., Fernandes et al. and the Canadian Forest Fire Behavior Prediction System, developed using field measurements. The performance of the ten models is evaluated, both qualitatively and quantitatively, by employing a range of dedicated statistical error metrics. It is shown that the performance of each model is affected by their specific characteristics, in conjunction with the characteristics of the experiments against which the models were evaluated. It is found that the model of Catchpole et. al. yields the lowest statistical error metric values. The empirical models that have been developed using field measurements exhibit significant discrepancies against the experimental data, due to the use of specific parameters regarding fuel type, scale and wind speed.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".