Implementation and spatialisation of the Canadian Fire Weather Index in the Veneto Region
Bibliographic record
Abstract
Fire danger rating systems are essential tools for fire management activities, allowing optimal allocation of resources both before and during the fire danger periods. Veneto Region's Forest Service is testing the Canadian Forest Fire Weather Index (FWI) System to assess fire intensity, accounting for the effect of wind and the moisture content of inflammable material. The following steps were taken to apply the FWI system: (a) selection of the smallest number (ideally 10 - 15) of weather stations to obtain input data. Principal Components Analysis was carried out on 62 time-series of 30 years (1960-1990), including mean monthly temperature (minimum and maximum) and rainfall. The results highlighted two principal directions of climatic variability that were interpolated by the co-kriging method, allowing to delineate 11 relatively homogeneous areas in the Veneto Region. One station representative of each area was chosen to provide daily data for computing the daily fire danger index by the Regional Rating Service; (b) automation of the FWI system. A SAS v.9.1® application runs the calculations and generates a regional map of daily fire danger for the Forest Service personnel. Graphics and tabular data are also available via intranet.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".