A classification scheme to determine wildfires from the satellite record in the cool grasslands of southern Canada: considerations for fire occurrence modelling and warning criteria
Bibliographic record
Abstract
Abstract. Daily polar orbiting satellite thermal detections since 2002 were used as the baseline for quantifying wildfire activity in the mixed grass and agricultural lands of southernmost central Canada. This satellite thermal detection record includes both the responsible use of fire (e.g. for clearing crop residues, grassland ecosystem management, and traditional burning), as well as wildfires in grasslands and agricultural lands that pose a risk to communities and other values. A database of known wildfire evacuations and fires otherwise requiring suppression assistance from provincial forest fire agencies was used to train a model that classified satellite fire detections based on weather, seasonality, and other environmental conditions. A separate dataset of high-resolution (LANDSAT 8 thermal anomalies) of responsible agricultural fire use (e.g. crop residue burning) was collected and used to train the classification model to the converse. Key common attributes of wildfires in the region included occurrence on or before the first week of May with high rates of grass curing, wind speeds over 21 km/h, relative humidity values typically below 40 % and fires that are detected in the mid-afternoon or evening. Overall, grassland wildfire is found to be restricted to a small number of days per year, allowing for the future development of public awareness and warning systems targeted to the identified subset of weather and phenological conditions.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".