Evaluation of the Canadian fire weather index system in an eastern Mediterranean environment
Bibliographic record
Abstract
Abstract The Fire Weather Index module of the Canadian Forest Fire Danger Rating System (CFFDRS) was evaluated during two consecutive fire seasons in the Mediterranean environment of Crete, Greece. The Duff Moisture Code (DMC), the Drought Code (DC), the Buildup Index (BUI) and the Fire Weather Index (FWI) were highly correlated to fire occurrence but only moderately to area burned. Logistic regression was applied in order to classify the FWI values into fire danger classes appropriate for the Mediterranean environments, as follows: 0–38 Low, 39–48 Moderate, 49–59 High, > 60 Extreme. The new classification was necessary because the existing Canadian fire danger classes were found inapt for the dry and extremely fire prone eastern Mediterranean climate of Crete. After the modification, the fluctuation of the FWI values predicted more successfully the days of high fire risk, as proved by the actual fire occurrence. High correlation was found between measured litter (L layer) moisture values and those predicted by the Fine Fuel Moisture Code (FFMC). The use of an equilibrium duff moisture content value lower than 20% in Mediterranean environments, would probably improve the Duff Moisture Code (DMC) predictions. The Drought Code (DC) was poorly correlated to the upper soil moisture content. Overall, the FWI demonstrated several aptitudes related to its potential use as a meteorological fire danger rating index in Mediterranean regions. However, long–term studies are necessary to determine the precise range of each fire danger class according to fire occurrence data. Copyright © 2010 Royal Meteorological Society
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".