Modelling of forest fuel and weather effects on fire behavior in the oak forests of Southern Sweden
Bibliographic record
Abstract
We modelled fire spread and fuel consumption as a function of fuel and weather conditions in Southern Swedish oak-dominated forests, using a dataset of 105 ignition experiments. We also tested whether regionally modelled and downscaled indices of fire weather provide a realistic assessment of fire behavior. Models fed with on-site weather data predicted 38% variability in fire spread and 68% in fuel consumption. Wind of 1.5 m/sec and above and the temperature of 15° C and higher or the wind speed of 2.0 m/sec and above with relative humidity below 35% marked boundary conditions discriminating between situations with the rate of fire spread 1.03 m/min and those when a fire spread reached 1.38 m/min and above. Fuel water content (FWC) below 50% promoted fuel consumption. A higher proportion of oak fuels increased fuel consumption and was positively correlated with fire-line intensity, but did not affect the fire spread. The regionally modelled Duff Moisture Code (DMC) and the Fine Fuel Moisture Code (FFMC) were poor predictors of fire behavior during the experiments.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".