Alberta’s 2023 wildfires: context, factors, and futures
Bibliographic record
Abstract
Wildfires burned an estimated 2.2 million hectares in Alberta in 2023. We describe key attributes of the fires relative to historical fires and fire seasons and offer a perspective on potentially influential factors. Thirty-six large fires ≥10 000 ha generated 95% of annual area burned. Individually, these fires exhibited sizes, fire weather, and behaviour consistent with historical fires; there were simply far more of them in 2023. Thirteen fires reported in early May were ignited by lightning and reached final sizes ≥10 000 ha, revealing a previously unrecognized threat. Historically, large lightning-ignited fires reported before mid-May occur just once per decade on average. Collectively, 18 large fires reported in early May coincided with drier conditions compared with 18 large fires reported after mid-May. Early May fire weather was also warmer and drier than historical weather. The early May fire group was a temporally concentrated outbreak in west-central Alberta and coincided with extreme potential rate of fire spread. Large fires reported after mid-May were intermittent through to September, concentrated in northern regions and coincided with extreme potential for fuel consumption. Individually, these two spatiotemporal modes of fire season severity (outbreak, intermittent) produced annual burned areas on par with historical extremes. Together, the 2023 multi-modal pattern of fire season severity amplified area burned far above anything previously recorded. Potential contributing factors include climate warming, hemispheric teleconnections, phenology and exhaustion of suppression resources. Implications for future fire seasons, research and management are discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".