Supplementary material from "When is fire weather extreme enough for active fire spread in Canada?"
Bibliographic record
Abstract
ABSTRACT A spread day is defined as a day in which fires grow a substantial amount of area, usually during high or extreme fire weather conditions. Accurately identifying a spread day at various environmental conditions could help both our understanding of fire regimes as well as with forecasting and managing fires on the ground. Although spread days could occur within a spectrum of fire weather conditions, a threshold is important to fire management and fire research. This study explores the relationships between spread days and fire activity in the forested area of Canada by spatially and temporally matching daily fire growth to interpolated daily gridded fire weather between 2001 and 2021. Using accumulative area burned density functions, we identified the fire weather conditions for spread days by Canadian Ecozones both annually and seasonally. Using these identifiers as thresholds, we estimated how extreme fire weather needs to be for a spread day to occur, and the proportions of potential spread days that would most likely be realized in real fire spread at various Canadian Ecozones. Our results showed that the median level fire conducive weather conditions are sufficient to support active fire growth, and on average about 22 - 30% of such days may be realized in real fire spread at various Canadian Ecozones.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.521 | 0.100 |
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".