Managing the Safety of Ice Covers Used for Transportation in an Environment of Climate Warming
Bibliographic record
Abstract
Cet article propose une revue des risques inhérents à la gestion sécuritaire des routes de transport sur couvert de glace. La plus longue et la plus sollicitée des routes de glace du Canada dessert l'industrie minière de diamant dans la partie centrale des Territoires du Nord-Ouest et s’étend jusqu’à la région de Kitikmeot au Nunavut. La tendance climatique au réchauffement que vit le Canada a pour effet de raccourcir la saison d’opération de ce type de route à un moment où la demande du trafic est proche de la limite opérationnelle et tend encore à s’accroître. L’article décrit les améliorations apportées à l'efficacité des routes de glace par le développement de technologies pour améliorer la capacité structurale et par une gestion rigoureuse des risques. Ces améliorations permettent de gérer efficacement une plus grande incertitude sur les conditions d’exploitation tout en maintenant la priorité sur la sécurité des usagers. This paper reviews some of the unique geohazards inherent in developing safe operating procedures for infrastructure reliant on ice covers. Canada’s longest and most heavily used ice road services the diamond mining industry in central Northwest Territories extending into the Kitikmeot region of Nunavut. A climatic trend to a diminished operating season is evident at a time when traffic demand is near the operational limit and future traffic growth is predicted. The paper describes improvements in ice road efficiency, load capacity and risk management that have been adopted to cope with greater uncertainties while continuing to put operator safety as the highest priority.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".