Case studies of Wildfire Evacuations in Canada Between 2020 and 2023 Using Publicly Available Sources
Bibliographic record
Abstract
An increasing number of communities are affected by wildfires in the wildland-urban interface (WUI) in Canada. While statistical information about wildfire evacuations in Canada is available, systematic descriptions of individual incidents are rare. However, case studies can be useful to illustrate and understand aspects unique to affected communities. Using an established reporting template, this paper provides an overview of five case studies that led to community evacuations across Canada between January 2020 and August 2023: (1) the White Rock Lake wildfire in 2021 (British Columbia), (2) the Edson Forest Area wildfire in 2023 (Alberta), (3) the Lebel-sur-Quévillon wildfire in 2023 (Québec), (4) the Tantallon wildfire in 2023 (Nova Scotia), and (5) the Behchokǫ̀-Yellowknife wildfire in 2023 (Northwest Territories). Information from publicly available resources was used to describe the communities, environmental conditions, incidents and evacuation. Information gaps where no data was publicly accessible are also highlighted. The case studies suggest that communities faced several challenges during the wildfire evacuations, most commonly related to communication. The case studies help better understand the context in which wildfire evacuations occur and how communities respond and manage wildfire evacuations in Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".