The 2022 fire season over Europe
Bibliographic record
Abstract
Over the summer of 2022, Europe experienced exceptional wildfire activity, with fires occurring more frequently and intensively, mainly in Spain, France, and Portugal. Together these countries registered more than 470 000 hectares of the total 786 000 burnt area in the European Union, accordingly to the estimates of the European Forest Fire Information System (EFFIS) for this fire season. Southern Europe is a widely known climate change hotspot resulting in heatwaves, droughts, and wildfire activity (increase in the number and severity of fires, burnt area, and longer fire seasons) although severe droughts and heatwaves have been expanding and worsening in central and northern Europe, increasing fire risk. This work aims to evaluate how extreme the 2022 fire season was when compared with the period 1979-2021 over Europe. The proposed methods comprise the analysis of fire-related products and atmospheric variables to evidence the fire-prone weather conditions. The European Centre for Medium-Range Weather Forecast (ECMWF) ERA5 reanalysis dataset of Fire Weather Index (FWI) and air temperature, relative humidity and wind products are used. FWI is part of a dataset from the Canadian Fire Weather Index System, and is defined as a numerical rating of the potential frontal fire intensity, that indicates fire intensity by combining the rate of fire spread with the amount of fuel being consumed. The Standardized Precipitation Evapotranspiration Index (SPEI) at time-scales of 1 to 6 months was used to assess drought conditions. Results highlight the new fire dynamics in Europe since climate change effects are leading to new emergent hot spots (central and northern Europe), not so well known as the Mediterranean Basin. This is extremely important to allow the assessment of fire danger activity as well as the characteristics of wildfires and improve the monitoring, planning, and mitigation activities.
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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 | 0.001 | 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) | 0.002 | 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".