Fire activity in Portugal and its relationship to weather and the Canadian Fire Weather Index System
Bibliographic record
Abstract
The relationships among the weather, the Canadian Fire Weather Index (FWI) System components, the monthly area burned, and the number of fire occurrences from 1980 to 2004 were investigated in 11 Portuguese districts that represent respectively 66% and 61% of the total area burned and number of fires in Portugal. A statistical approach was used to estimate the monthly area burned and the monthly number of fires per district, using meteorological variables and FWI System components as predictors. The approach succeeded in explaining from 60.9 to 80.4% of the variance for area burned and between 47.9 and 77.0% of the variance for the number of fires; all regressions were highly significant (P < 0.0001). The monthly mean and the monthly maximum of daily maximum temperatures and the monthly mean and extremes (maximum and 90th percentile) of the daily FWI were selected for all districts, except for Bragança and Porto, in the forward stepwise regression for area burned. For all districts combined, the variance explained was 80.9 and 63.0% for area burned and number of fires, respectively. Our results point to highly significant relationships among forest fires in Portugal and the weather and the Canadian FWI System. The present analysis provides baseline information for predicting the area burned and number of fires under future climate scenarios and the subsequent impacts on air quality.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".