Defining a novel method to undertake sectoral risk management under climate change: a case study of intermodal transportation in the Peel Region, Ontario, Canada
Bibliographic record
Abstract
All levels of government must prepare for an increase in adverse weather events related to climate change. Developing resilient transportation infrastructure is critical to minimizing disruptions, economic loss, and human health impacts. A challenge for national and regional governments, however, is understanding how to prioritize investments given risk levels and limited resources. This study proposes a framework, using the Region of Peel, Canada as a case study to identify and prioritize key risks in a critical economic sector for the region: intermodal goods movement. The framework integrates projected changes in weather patterns, estimating the damage to infrastructure, interruption of economic activity, and adverse impacts on the workforce, accounting also for impacts on communities, for sound policy formulation. The framework will underpin a data collection plan to inform future policy and investment in strengthening adaptation and resilience to the most likely hazards affecting goods movement. The framework was designed with a view to being easily adapted to other sectors and regions.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".