An approach to operational forest fire growth predictions for Canada
Bibliographic record
Abstract
This paper presents an operational approach to predicting fire growth for wildland fires in Canada. The approach addresses data assimilation to provide predictions in a timely and efficient manner. Fuels and elevation grids, forecast weather, and active fire locations are entered into a fire-growth model; then predicted fire perimeters are mapped and presented on the web. The Moderate Resolution Imaging Spectroradiometer (MODIS) and the National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA/AVHRR) satellite-based detection systems are used to detect current wildland fires (referred to as hotspots). For selected regions, fire-growth simulation environments are assembled. Fuel type data from several fire management agencies are available in grid format at a resolution of 100 m or less; in areas where such data are not available, a national fuels map based on Satellite Pour l’Observation de la Terre Vegetation sensor (SPOT VGT) land cover and forest inventory is used. Similarly, terrain data are available from a variety of sources. Current hotspots are used as ignition points while past hotspots are used to delineate area burned. Surface wind, temperature, and dew-point values (forecast by Environment Canada) are used to determine the fire weather conditions at the fire location. A case study of two large fires in Canada consisting of 54 fire simulation days is used to test these hypotheses.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".