Assessing fire danger synchronicity in Europe
Bibliographic record
Abstract
This contribution seeks to better understand recent changes in synchronous fire danger across Europe that can overwhelm fire suppression capacity. We analyze the spatio-temporal synchronicity of fire danger in Europe over the period 1979-2021 based on the Canadian Fire Weather Index, one of the most commonly used fire indices globally (FWI; Vitolo et al. 2020). The daily synchronicity index indicates the total area with a level of FWI above 50, that represents the extreme fire danger threshold as classified by the European Forest Fire Information System (EFFIS). The annual mean surface affected by synchronicity extreme fire danger increased by about 100000 km2 over the 42-year-long study period (i.e. 57% of the mean historical value). The expansion of synchronized fire potential can compromise fire management efforts.ReferencesVitolo, C., Di Giuseppe, F., Barnard, C., Coughlan, R., San-Miguel-Ayanz, J., Libertá, G., & Krzeminski, B. (2020). ERA5-based global meteorological wildfire danger maps. Scientific data, 7(1), 1-11.AcknowledgmentsWe acknowledge funding through the project ONFIRE, grant PID2021-123193OB-I00, funded by MCIN/AEI/ 10.13039/501100011033. A.G. thanks the Ministerio de Ciencia, Innovación y Universidades of Spain for PhD contract FPU19/06536.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".