Vulnerability assessment of Alberta's provincial highway network
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it