Wildland fire evacuations in Canada from 1980 to 2021
Bibliographic record
Abstract
Background Every year, people in Canada are evacuated due to wildland fires to avoid death, injury, and illness from fire and smoke events. Aims In this paper, we provide an overview of evacuations recorded in the Canadian Wildland Fire Evacuation Database between 1980 and 2021. Methods Our analysis covers evacuations in Canada from 1980 to 2021. We provide summary statistics including number of evacuations and evacuees, evacuation duration, seasonality, evacuation causes, community types, structure losses, and fatalities. We also investigate temporal and spatial patterns. Key results Between 1980 and 2021, there were 1393 wildland fire evacuation events with 576,747 people evacuated. During this period, there was an overall increase in frequency of evacuations, number of evacuees, and duration of events. Structure loss occurred during 194 evacuation events, with 4105 homes burned. We estimate wildland fire evacuations cost at least CAD3.7 billion (excluding structural losses), jumping to CAD4.6 billion if we include productivity losses. Indigenous peoples are disproportionately impacted in wildfire evacuations compared to the general Canadian population. Conclusions Wildland fire evacuations continue to occur across Canada and are increasing. Implications The findings from this study give us a better understanding of the characteristics of wildland fire evacuations, which can help guide emergency management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".