Impact of climate change on area burned in Alberta’s boreal forest
Bibliographic record
Abstract
Eight years of fire weather data from sixteen representative weather stations within the Boreal Forest Natural Region of Alberta were used to compile reference weather streams for low, moderate, high, very high and extreme Fire Weather Index (FWI) conditions. These reference weather streams were adjusted to create daily weather streams for input into Prometheus – the Canadian Wildland Fire Growth Model. Similar fire weather analyses were completed using Canadian Regional Climate Model (CRCM) output for northern Alberta (174 grid cells) to generate FWI class datasets (temperature, relative humidity, wind speed, Fine Fuel Moisture Code, Duff Moisture Code and Drought Code) for 1 ×, 2 × and 3 × CO2 scenarios. The relative differences between the CRCM scenario outputs were then used to adjust the reference weather streams for northern Alberta. Area burned was calculated for 21 fires, fire weather classes and climate change scenarios. The area burned estimates were weighted based on the historical frequency of area burned by FWI class, and then normalized to derive relative area burned estimates for each climate change scenario. The 2 × and 3 × CO2 scenarios resulted in a relative increase in area burned of 12.9 and 29.4% from the reference 1 × CO2 scenario.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".