Use of MERIS and AATSR data in previsional fire danger index computation system for alpine regions
Bibliographic record
Abstract
The FWI-tel instantaneous is a system for computing a forest fire risk index; it elaborates instantaneous weather maps to evaluate fire risk indicator on alpine regions. This system is based on the previsional Canadian Fire Weather Index (FWI) adjusted for continental Europe latitudes and climatology and adapted to alpine regions orography. FWI is a meteorological index, which uses as input data meteorological forecasts (or analysis), satellite remote sensed and weather radar data. In the current version of the system, air temperature and relative humidity data come from MODIS and AIRS sensors, rain data come from MeteoSwiss weather radars and wind data come from BOLAM Numerical Weather Prediction models. In particular, relative humidity data are characterized by a very coarse spatial resolution which makes difficult over complex-orography regions the precise localization and evaluation of the fire danger index. Furthermore, MERIS relative humidity sensed data, are used as new input for the FWI-tel instantaneous system. The aim of using such data is to compare the results obtained with them with the ones obtained by using AIRS data. In this way it would be possible to evaluate any improvement in the instantaneous forest fire danger index using MERIS data, with respect to AIRS, in order to obtain a more efficient real time territory monitoring system for forest fires over complex-orography regions. As soon as soil moisture data from the SMOS satellite will be available they too will be included in the index computation. Moreover MERIS vegetation index will be included in a future version of the index. 1.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".