Analysis of the Canadian Fire Weather Index during large fires in Croatian Adriatic
Bibliographic record
Abstract
Wildland fires, especially the large ones, are becoming a growing problem in the climate changing world. More frequent and long-lasting drought conditions accompanied by high temperatures and heat waves, significantly increase fuel flammability, particularly during the summer period. The wildland fire occurrence and behaviour are to a large degree weather driven and thus strongly depend on the meteorological parameters such as humidity, temperature, precipitation, and wind speed, as well as on the amount of fuel load. The relationship between weather and fire occurrence and behaviour is included in Canadian Fire Weather Index system, which has been used in Croatia for fire risk assessment since 1982. In this paper, the characteristics of the Fire Weather Index components are analysed for large fires in the Adriatic region of Croatia. Fire weather indices were evaluated for 103 wildland fires with a burned area over 400 ha that occurred during summer fire seasons in the period from 2003 to 2021. Obtained median values of the moisture indices, as well as the fire behaviour indices (FFMC 93, DMC 139, DC 649, ISI 13, BUI 182 and FWI 45) showed values designated as high and very high in the available literature. The climate change will continue to increase the fire risk, and thus the possibility of large fires, so this analysis can provide a baseline for improvements and recalibration of the fire danger classes in the Adriatic area of Croatia. Along with the improved fire weather warnings, this will give a better and more accurate information about the increased wildland fire risk and the possibility of large fires.
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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 | 0.002 | 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) | 0.000 | 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".