Monthly adaptations of the Drought Code reveal nuanced fire–drought associations in montane forests with a mixed-severity fire regime
Bibliographic record
Abstract
We compared three monthly adaptations of the daily Drought Code (DC) of Canada’s Fire Weather Index System and applied them to interpret drought conditions associated with historical fires in montane forests of south-eastern British Columbia. The three adaptations were compared with the monthly mean DC calculated from daily values for the Palliser fire-weather station. Two adaptations improved on the existing Monthly DC calculated from monthly climate data by (1) accounting for overwinter drying and an early start to the fire season, and (2) improving estimates of effective precipitation. Using a cross-dated fire-scar record from 20 sites in montane forests surrounding the Palliser station, we found significant fire–drought associations from June to August with all adaptations, and significant associations in April and May with the two new adaptations. Of the 17 fire years from 1901 to 2013, 6 years had low initial drought conditions that increased late in the fire season, and 5 years had high drought conditions throughout the fire season. We conclude that variable drought within and among fire seasons influenced fire severity. Our findings provide a connection between modern drought indices used to rank fire danger and drought effects on the historical mixed-severity fire regime in montane forests of south-eastern British Columbia.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".