Assessment of fire weather index calculations in Europe
Bibliographic record
Abstract
Wildfires caused by extreme weather conditions e.g high temperatures and drought have been increasing both in frequency and magnitude in the recent years. Due to the changing climate also regions with few recorded forest fires in the past are likely to be more frequently affected by wildfires. These wildfires have many social, economic, and environmental impacts. The EU funded SAFERS project, Structured Approaches for Forest Fire Emergencies in Resilient Societies, is creating an open and integrated platform featuring a forest fire decision support system in order to support societies becoming more resilient when acting against forests fires. The platform will, among other information, provide weather forecasts and forest fire indices to assess the risk of forest fires. Using the Canadian Forest Fire Weather Index (FWI) system [Van Wagner, 1974], fuel moisture (FFMC, DMC, DC) and fire behaviour indices (ISI, BUI, FWI) can be calculated using solely meteorological parameters (temperature, relative humidity, wind speed, and 24-hour precipitation). The Canadian FWI has been proved useful for forest fire risk assessment in different regions and has been adapted by multiple meteorological agencies worldwide. In our study, we use high resolution deterministic weather forecasts, as well as medium-range and extended-range weather forecasts provided by the European Centre for Medium-Range Weather Forecasts to calculate the FWI indices for up to 3, 15 and 46 days ahead, respectively. In our contribution we will present preliminary results of FWI verification and calibration methods. References:Van Wagner, C. E.: Structure of the Canadian Forest Fire Weather Index, Departmental Publication 1333, Environment Canada, Canadian Forestry Service, Petawawa Forest Experiment Station, Chalk River, Ontario, p. 49, 1974.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".