Predicting crown fire behavior to support forest fire management decision-making
Bibliographic record
Abstract
Fire behavior models are an important component of decision support systems for fire management. This paper describes the modeling of two fundamental crown fire behavior features: the onset of crowning and the spread rate of crown fires. The present study is based largely on the database used in the development of the Canadian Forest Fire Behavior Prediction System. The dataset used in the study consisted of 73 experimental fires in various coniferous forest fuel complexes, 40 of which were classified as crown fires. These fires cover a wide spec- trum of fire environment conditions and fire behavior characteristics, with rates of spread rang- ing from 0.5 - 49.4 m/min, and fireline intensity from 62 - 45,200 kW/m. Crown fire initiation was modeled through a logistic regression approach using 10-m open wind speed, fuel strata gap or height to live crown base, a surface fuel consumption class, and an index of fine dead fuel moisture content as independent variables. Spread rates for active and passive crown fires were modeled through multiple non-linear regression analysis following physical reasoning. Inde- pendent variables used in the crown fire spread models were 10-m open wind speed, canopy bulk density and again the index of fine dead fuel moisture content. The crown fire initiation model correctly predicted 85 % of the cases in the dataset used for its construction. The active crown fire spread model yield a R2 of 0.61. The wide variation in fuel complex structure and fire be- havior in datasets used to build the crown fire initiation and rate of spread models gives confi- dence that the models might work well in fuel complexes different from the original ones, given an adequate description of the physical characteristics of the fuel complex.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 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".