Evaluating ENSO-driven risks to strengthen transportation system resilience of Canadian provinces
Bibliographic record
Abstract
This paper examines the effects of El Niño/La Niña Southern Oscillation (ENSO) on Canadian transportation networks, including road, rail, transit, and active transportation systems. It highlights hazards such as flooding, drought, wildfires, and storm surges, particularly in regions like British Columbia, Alberta, and the Maritimes. The study discusses the challenges for emergency managers, transportation operators, and planners in developing mitigation and adaptation strategies. As climate change intensifies ENSO impacts, understanding these effects is crucial for strengthening infrastructure resilience. While focused on Canada, the findings also have implications for other Northern Hemisphere regions. The study emphasizes the need for further research on ENSO-climate change linkages and enhanced training for transportation professionals. ENSO applications provide a strategic approach to bridging short-term weather events with long-term climate trends, offering valuable insights for improving the adaptability of Canada's transportation systems in the face of increasing climate variability.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".