Fire Weather Index (FWI) for Estonia for growing seasons 2018 and 2019
Bibliographic record
Abstract
The Canadian Forest Fire Weather Index (FWI) System (Van Wagner 1987) is a weather-based means of calculating potential fire conditions. Canadian research on forest fire danger rating was started in the 1920s, but in recent decades, FWI is widely used in Europe. The FWI System depends solely on weather measurements taken each day at noon local time. Daily inputs to the system consist of temperature (°C), relative humidity (%), wind speed (km/h), and precipitation (mm) over the past 24 hours. The FWI System outputs three moisture codes: the Fine Fuel Moisture Code (FFMC), the Duff Moisture Code (DMC), and the Drought Code (DC), with higher values indicating drier conditions and greater fire danger. The FWI System also generates three fire behavior outputs based on the above indices: the Initial Spread Index (ISI), the Build-Up Index (BUI), and the Fire Weather Index (FWI), with higher values indicating elevated fire danger (Van Wagner 1987). This dataset creation was a part of RITA1/02-52 "Use of remote sensing data for elaboration and development of public services (1.01.2019−31.12.2020)" and its work-package “Prevention and suppression of wildfires“. Project adopted Canadian methodology and investigated possibility of FWI in Estonian meteorological service for wild fire prevention routings. C. E. Van Wagner (1987). Development and Structure of the Canadian Forest Fire Weather Index System. Canadian forest service, Forestery Technical report 35, Ottawa
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".