Towards the operational implementation of the Fire Weather Index FWI based on the High-Resolution WRF Model
Bibliographic record
Abstract
Wildfires represent an important issue in Argentina and their management strongly depends on the possibility of having all the resources ready to be used at the right time. To this purpose, the availability of fire danger indices becomes essential. In previous work, the authors implemented the Australian index FFDI (Forest Fire Danger Index) for the Southern Cone, based on a weather forecast model at 16 km of spatial resolution. In the present paper we present the design of a platform for the generation of a fire danger forecast, based on the Canadian index FWI (Fire Weather Index), which is the one used by the Federal System of Wildfire Management of Argentina. Through this platform, FWI is automatically estimated based on the weather forecast of 24 and 48 hs of the Weather Research and Forecast (WRF), run at 4 km spatial resolution for the central region of Argentina. Data of temperature, wind, air relative humidity and rainfall of the previous 24 hs are necessary to calculate FWI. Even though the system is in an experimental phase, preliminary results show good ability of our FWI product to identify areas with higher probability of wildfires. The system will include a component of visualization, automatically exporting raster outputs to web pages (PNG format) and webgis servers (Geotiff format) available at the institutions involved in this project, where the product will be available to download.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".