Reconstructing an Emergency Evacuation by Ground and Air the wildfire in Fort McMurray, alberta, Canada
Bibliographic record
Abstract
The May 2016 wildfire in Fort McMurray in northern Alberta, Canada—the costliest wildfire disaster in Canadian history—led to an areawide evacuation by road and air. Traffic count and flight data were used to assess the characteristics of the evacuation, including estimates of people movements by vehicle and aircraft. The vehicle counts were compared first with historic values to examine traffic patterns and were then used to create an evacuation response curve, which revealed an expected S-shaped curve and highlighted how quickly the evacuation occurred. Finally, data for people evacuated by aircraft were combined with data for people evacuated by ground vehicle to construct a curve of the cumulative number of evacuees leaving the region. This study identified several key implications for evacuation planning and operations. The decision to evacuate residents to temporary shelters in the north was instrumental in the quick removal of everyone from immediate danger via all possible exits. Although an unplanned contraflow operation added roadway capacity out of Fort McMurray, the underuse of the secondary route suggested that the management of traffic routing might have reduced congestion. The evacuation response curve emphasized the volatility of the wildfires, with the resulting evacuations occurring under conditions of a greater immediacy than hurricane evacuations. Finally, the significant role of air transportation in this evacuation indicated that multimodal emergency evacuation plans may be critical for remote communities and sparse networks. These findings may be applied to evacuation planning and policy to improve the efficiency and efficacy of evacuations.
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 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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".