Variation in the Canadian Fire Weather Index Thresholds for Increasingly Larger Fires in Portugal
Bibliographic record
Abstract
Forest fire management relies on fire danger rating to optimize its suite of activities. Limiting fire size is the fire management target whenever minimizing burned area is the primary goal, such as in the Mediterranean Basin. Within the region, wildfire incidence is especially acute in Portugal, a country where fire-influencing anthropogenic and landscape features vary markedly within a relatively small area. This study establishes daily fire weather thresholds associated to transitions to increasingly larger fires for individual Portuguese regions (2001–2011 period), using the national wildfire and Canadian fire weather index (FWI) databases and logistic regression. FWI thresholds variation in relation to population density, topography, land cover, and net primary production (NPP) metrics is examined through regression and cluster analysis. Larger fires occur under increasingly higher fire danger. Resistance to fire spread (the fire-size FWI thresholds) varies regionally following biophysical gradients, and decreases under more complex topography and when NPP and occupation by flammable forest or by shrubland increase. Three main clusters synthesize these relationships and roughly coincide with the western north-central, eastern north-central and southern parts of the country. Quantification of fire-weather relationships can be improved through additional variables and analysis at other spatial scales.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".