Vulnerability assessment of Alberta's provincial highway network
Bibliographic record
Abstract
Within their emergency planning and management roles, it is critical for transportation authorities to understand the characteristics of the transportation network and the communities it serves. The northeastern section of the province of Alberta, Canada has a very limited roadway network and is remote from major population centers, yet also has a relatively large population concentration due to the oil and gas industry. It is also prone to wildfires, with subsequent community evacuations every year in the summer months. This paper is a case study of the application of several network analysis measures (related to network topology, community accessibility, and transportation facility characteristics) to this wildfire-prone region, to better understand the region's vulnerability in the face of emergency evacuation and facility disruption. Our results show communities in the Regional Municipality of Wood Buffalo are highly vulnerable to facility disruptions while accessibility to major centers during evacuation is relatively low. Our results also determine critical communities with respect to network vulnerability, and locations for interim emergency supplies. Despite the concentrated populations supporting oil and gas extraction, historical indigenous communities, and the growing prevalence of wildfires and evacuations, justification of transportation infrastructure investments is difficult in this remote area. The findings demonstrate the need for provincial and federal emergency management plans that incorporate the use of existing intermodal infrastructures (i.e. aerodromes) as an alternate means of transport connecting impacted communities. The findings also provide guidance for traffic management planning, strategic placement of emergency services, and identifying where infrastructure investments are most critical.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".