YOU AND WHOSE ARMY? A REVIEW OF THE JANUARY 1999 TORONTO SNOW EMERGENCY. IN: WEATHER AND TRANSPORTATION IN CANADA
Bibliographic record
Abstract
Heavy snowfall presents major challenges to large cities. This paper provides a comparison of observations from the literature on urban snowfall hazards with observations from a case study of the January 1999 Toronto snow emergency in which a series of winter storms affected the region. The case study demonstrates how repeated heavy snowfalls, along with cold temperatures, high winds, drifting snow, and human factors, exceeded the capacity of systems to maintain reliable transportation services. Loss of mobility was the dominant feature of the Toronto snow emergency. This case study reveals concerns regarding the methods employed in early snow hazard research. The case study and literature review suggest that further analysis is needed to improve methods for understanding snow hazard vulnerabilities, estimating impacts and modeling relationships between winter weather and indices of urban activity. The costs and benefits of traditional snow hazard responses versus alternative measures such as intentional restrictions on mobility and demand management options should also be evaluated.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".