Bibliographic record
Abstract
Vegetation fires are among the most dangerous natural hazards. In Croatia, they are most common on the Adriatic coast and in the areas near the Adriatic, especially in summer when the peak of the fire season occurs. Meteorological risk of vegetation fires in Croatia is primarily assessed with the Canadian Forest Fire Weather Index and derived fire danger. However, due to the rare spatial and temporal availability of these data as well as the effects of climate change resulting in frequent enhanced fire danger during the summer, the need for additional risk assessment tool has emerged. In that sense, the fire weather warnings were introduced ten years ago and since then have been constantly improved and adapted to the needs of the firefighting community. The aim is to warn of weather conditions that can lead to the rapid spread and unpredictable behavior of the vegetation fires. As a basic condition, fire weather warnings are issued when the fire danger classes from the Canadian Forest Fire Weather Index are high and very high. Except the meteorological parameters of the Fire Weather Index, the most important of which is wind, the behavior and spread of vegetation fires can be further influenced by atmospheric turbulence and instability in the dry air near the ground and in the lower atmosphere. These parameters also depend on the orography of the terrain and are not included in the Fire Weather Index. Two years ago, three levels of warnings were defined, the thresholds of which depend on the values and duration of wind, turbulence and instability. In addition to the detailed explanation of the method for issuing fire weather warnings, the paper presents textual and graphical warning examples, describes five critical fire weather patterns when conditions for issuing warning are the most common, and in the end gives a brief overview of warnings statistics and the most recent evaluation. According to the feedback from the firefighting community, the warnings proved to be important in the organization of supervisory and preventive activities, as well as fire suppression activities in case of vegetation fire ignition. The evaluation also shows that most of the significant vegetation fires burned in the days and at the locations for which warnings were issued.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".