Do fire danger classes in Croatia need calibration?
Bibliographic record
Abstract
The Canadian Fire Weather Index (FWI) System was calibrated for Croatian Adriatic some 40 years ago. Five fire danger classes were introduced, which since then have been in use for fire danger assessment during the fire season. In the presence of climate change the fire danger is continuously growing, leading to the need for a review of the Fire Weather Index System and eventually a new calibration. In this paper, a simple analysis of the highest danger class was performed to determine whether there are reasons for revision of fire danger classes and possible introduction of the extreme danger class. The results show rather high occurrence of the very high danger class, 20% of cases in the fire season and 25% during the peak of fire season (July and August). The percentile values of FWI and BUI obtained by the large fire (burned area > 400 ha) analysis, especially the 50thpercentile, with a 3% occurrence rate in average, can be used as an indicator of the specific fire weather conditions that can be described with an extreme fire danger class.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".