Effects of wind velocity on predictions of wildland fire rate of spread models: A comparative assessment using surface fuel fire tests
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it