Spatio-temporal analysis of forest fires in Croatia
Bibliographic record
Abstract
Although the European Forest Fire Information System (EFFIS), provided by the Copernicus Emergency Management Service, offers three different methods for determining forest fire danger, the Canadian method is usually used and accepted in Croatia. The Canadian Fire Weather Index (FWI) estimates the forest fire danger level based on meteorological parameters (air temperature, humidity, wind speed and precipitation amount) related to 12 UTC for the given day at the meteorological station or to a grid point of a numerical weather prediction model.Thanks to the EFFIS statistics portal, it is possible to see the extent to which Croatia has been at risk from forest fires in recent years based on the areas burned and the number of fires. The Copernicus Climate Change Service (C3S) provides a much more detailed overview of the burned areas. The combination of data from the Climate Change Service and the Emergency Management Service can provide a better overview of forest fires in Croatia. The forest fire danger levels are analyzed spatially between different regions such as the continental, mountainous and Adriatic parts of Croatia. In order to find an appropriate duration of the fire season, the forest fires within and outside the fire season are listed. The aim of the spatio-temporal analysis is to show the most endangered areas and the seasonal trend of forest fires in Croatia.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".