Changes in the Seasonality of Fire Activity and Fire Weather in Portugal: Is the Wildfire Season Really Longer?
Bibliographic record
Abstract
The length of the fire season has not garnered much attention within the broad field of meteorological research on fire regime change. Fire weather research on the Iberian Peninsula is no exception in this case; there is no solid understanding on fire season lengthening in Portugal, although recent decades do suggest ongoing transitions. Based on a complete record of fire occurrence and burned area between 1980 and 2018, we first searched for consistent trends in the monthly distribution of fire activity. To determine day-scale changes, an exceedance date method based on annual cumulative burned area was developed. Results show an early onset of fire activity in a range of 23–50 days and no significant extension into autumn, suggesting that existing projections of the lengthening of the fire season in Portugal over the present century have been already achieved. Fire weather results show a trend in the cumulative Daily Severity Rating (DSR), with the last two decades (2000–2018) displaying an early build-up of meteorological fire danger in late spring and early summer. The detailed spatio-temporal analysis based on the daily Fire Weather Index (FWI) shows that June stands out with the largest increase (year-round) in days per month with an FWI above 38.3, the threshold above which fire conditions make suppression uncertain. This aggravated fire weather is likely sustaining early fire activity, thus contributing to a longer critical fire season.
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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 | 0.001 |
| 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) | 0.002 | 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".