Characterisation of initial fire weather conditions for large spring wildfires in Alberta, Canada
Bibliographic record
Abstract
We evaluated surface and 500-hPa synoptic weather patterns, and fire weather indices from the Canadian Forest Fire Danger Rating System for 80 large wildfires during 1990–2019 in Alberta that started in May and grew to over 1000 ha. Spread days were identified during the first 4 days of wildfire activity. We observed two distinct synoptic weather patterns on these days. Pre-frontal and frontal passage activity was the predominant feature associated with 48% of the calendar spread days. Strong south–south-east winds from a surface high centred east of Alberta (west of Hudson Bay) and supported by an upper ridge, and a surface low located south-west of the ridge occurred on 26% of the calendar spread days. Surface analysis indicates the spring wildfire season in Alberta is driven by very high to extreme Initial Spread Index, a rating of the expected wildfire rate of spread based on Fine Fuel Moisture Code and wind. Very high to extreme values of Buildup Index, a rating of the amount of fuel available for consumption, are not a prerequisite for large wildfires in May. For Alberta, this means large wildfires in May can occur after only a few days of dry, windy weather.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".