Analyzing meteorological risks to highway infrastructure in Saskatchewan
Bibliographic record
Abstract
Abstract Highway infrastructure is essential to Canada’s transportation system, supporting economic activity and regional connectivity. However, its sustainability is increasingly challenged by meteorological hazards. This study conducts a detailed spatial risk assessment of Saskatchewan’s major highways by analyzing six climate-related factors: flood-prone areas, precipitation mm d −1 ), snowfall (cm d −1 ), extreme temperatures (minimum and maximum in °C), and wind (maximum gust speed in km h −1 ). Using ArcGIS, hazard maps were developed and reclassified through three methods: equal-weighting, score-based assessment, and the analytical hierarchy process (AHP). Seasonal variations were also addressed by generating separate risk layers for winter and summer conditions. The results indicate that southern and south-central Saskatchewan especially around Regina and Saskatoon faces the highest cumulative climate risk. Conversely, northern regions show isolated high risks but minimal infrastructure impact due to sparse networks. The integrated risk maps provide actionable insights for transportation authorities to prioritize climate-resilient planning, reduce service interruptions, and improve long-term road network reliability across varying seasonal extremes.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".