Canada Under Fire – Drivers and Impacts of the Record-Breaking 2023 Wildfire Season
Bibliographic record
Abstract
The 2023 wildfire season in Canada was unprecedented in its scale and intensity. Spanning from late April to early November and extending across much of the forested regions of Canada, the season resulted in a record-breaking total area burned of approximately 15 million hectares, over seven times the historic national annual average. The impacts were profound with more than 200 communities evacuated (approximately 232,000 people), periods of dense smoke that caused significant public health concerns, and unprecedented demands on fire-fighting resources. The exceptional area burned can be attributed to several environmental factors that converged early in the season to enable extreme fire danger over much of the country. These factors included early snowmelt, interannual drought conditions in western Canada, and the rapid transition to drought in eastern Canada. Furthermore, the mean May-October temperature over Canada in 2023 was a staggering 2.2°C warmer than normal (1991-2020), enabling sustained extreme fire weather conditions throughout the fire season. These conditions led to a larger than normal proportion of very large fires (> 50,000 hectares), many having burned for months from the spring into the fall. Fires that started in May or June accounted for over two-thirds of the total area burned. Overall, the 2023 wildfire season in Canada was characterized by its exceptional scale and major societal impacts, setting new records and highlighting the increasing challenges posed by wildfires in the country.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".