Preparedness and Experiences of Evacuees from the 2016 Fort McMurray Horse River Wildfire
Bibliographic record
Abstract
The 2016 Fort McMurray Horse River Wildfire that caused the evacuation of more than 88,000 residents from the Regional Municipality of Wood Buffalo (RMWB), Alberta is the largest wildfire evacuation in Canadian history. This paper presents results of an online survey of 447 evacuees in June when some residents had returned to RMWB, and others were still living elsewhere. Results of this online survey show that many survey respondents were not aware of the high wildfire risk leading up to May 3rd and social cues indicated they should carry on as usual. Many respondents received little if any warning time to enable them to prepare. Most respondents had a vehicle, but traffic impediments caused some to run out of gas, food and water on the drive away from Fort McMurray. Most respondents stayed in more than one location, with most staying with friends and family for at least part of the evacuation. Some respondents faced challenges including financial difficulties, finding suitable accommodation, and dealing with insurance, and some received insufficient information. Help was offered to evacuees by a vast array of sources including organizations, businesses, communities, and residents. The results of this study show that most survey respondents were not prepared before they had to leave, which increased reliance on help provided by others.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".