Applicazione del Canadian Forest Fire Weather Index System nel contesto della Grande Regione Alpina (GAR): potenzialità e problematiche
Bibliographic record
Abstract
Wildland fires are not the main ecological disturbace in Northern Italy, but the need of operative personnel on the field justifies the research of new tools to ensure them a safe work environment. In this study the application of the Canadian Forest Fire Weather Index System has been analysed, with a focus on three Italian regions: Veneto, Aosta Valley and Lombardy. Both initial phase of implementation and advanced applications have been taken into account. The regions have been considered in the frame of the Greater Alpine Region (GAR), in order to provide a multi-scale approach. In particulare, it has been studied: 1) the initial implementation of the fire danger system, and the index spatialization in Veneto, followed by a pilot calibration in Verona province; 2) the evaluation of crown fires leading factors and production of models to predict the critical live fine fuels moisture in Aosta Valley; 3) preliminary evaluation of FWI System performance in regard to large fires (burnt area higher than 100 ha) in Lombardy. Results of the work are addressed to enhance the actual knowledge on winter fires and to underline their specificity
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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 | 0.001 | 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".