Assessment of fire seasonality, and evaluation of fire-weather relationship, and fire danger models in Italy
Bibliographic record
Abstract
The main aim of this work was to improve our understanding of wildfires in the Mediterranean context through the characterization of fire regime and the assessment of the main driving forces on fire occurrence. \n A preliminary hierarchical cluster analysis, based on the fire occurrence and weather data in the period 1985-2008 has allowed to identify 6 areas homogeneous for fire events/regime and climate. Subsequently, three specific chapters have been developed. In the first chapter, the fire seasonality has been assessed for each area; the analysis also demonstrated that some changes occurring during the examined period. Then, the fire-weather relationships have been characterized. In the last chapter, two common fire danger indexes (the Canadian Fire Weather Index FWI, and the Keetch-Byram Drought Index KBDI) have been used to study the fire danger across Italy and its potential to reproduce the fire occurrence. \nThe results improve our knowledge of wildfire occurrence in Italy. The fire regime under current climate conditions has been characterized with a statistical and descriptive analysis. In addition, the analysis of the relationships between fire occurrence, weather, and fire danger can be usefully applied to assess the impacts of climate changes on fire regimes in Italy.
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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) | 0.001 | 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".