Using fuel and weather variables to predict the sustainability of surface fire spread in maritime pine stands
Bibliographic record
Abstract
Thresholds for surface fire spread were examined in maritime pine ( Pinus pinaster Ait.) stands in northern Portugal. Fire sustainability was assessed after ignition of 2 m fire lines or in larger burns conducted in 10–15 m wide plots. The experiments were carried out from November to June in three fuel types: litter, litter plus shrubs, and litter with a nonwoody understorey. Moisture content of fine dead fuels, on-site weather variables, and descriptors of the fuel complex all had a highly significant influence on the probability of self-sustaining fire spread. A logistic model based solely on fuel moisture content correctly classified the fire sustainability status of 88% of the observations. Nonetheless, the subjectivity of the moisture of extinction concept was apparent, and further accuracy was achieved by the consecutive addition of fire spread direction (forward or backward), fuel type, and ambient temperature. Fully sustained fire spread, in opposition to marginal burns with broken fire fronts, was similarly dependent on fuel moisture but was affected also by fire spread direction and time since rain. The models can benefit fire research and fire management operations but can be made more practical if integrated in a fire danger rating system.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".