30-Year Canadian Fire Weather Index Simulations over Europe: CMIP6-Informed Temperature and Precipitation Perturbations
Bibliographic record
Abstract
The dataset integrates a 30-year Canadian Fire Weather Index (FWI), generated using the Global ECMWF Fire Forecast model, forced by ERA5 reanalysis data (1981-2010). These simulations incorporate perturbations in temperature and precipitation forcings based on CMIP6 climate projections under the SSP2-4.5 medium mitigation scenario. The perturbed forcings were produced by modifying the daily temperature and precipitation data from ERA5 for the period of control from 1980 to 2010, using monthly factors that were estimated from a combination of climate change signals obtained from CMIP6 multi-model simulations, along with mean annual perturbations. The perturbation for temperature was constructed as an additive factor, varying from 0°C to 5°C with an increment of 1°C, added to the projected temperature under the SSP2-4.5 climate scenario. The perturbation for precipitation was constructed as a multiplicative factor, ranging from 0.6 to 1.6 with an increment of 0.2. The dataset offers daily FWI values across Europe, with a 31 km resolution, for each perturbation scenario over three decades.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.009 |
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".