An approach for including polar class vessels in the Canadian Arctic shipping regulations
Bibliographic record
Abstract
Transport Canada has indicated that they will be updating the Arctic Shipping Pollution Prevention Regulations (ASPPR) which are used to regulate shipping in the Canadian Arctic. There are several reasons for this. The Canadian Hydraulics Centre of the National Research Council of Canada (NRC-CHC) has performed significant research to evaluate the veracity of the two existing systems (Zone-Date System and Ice Regime System) that form part of these Regulations. The research clearly showed that neither system properly takes into account the ice conditions and the allowable areas for different vessels in Canada's Arctic waters. Further, these Regulatory Systems are based on various classifications for ice-strengthening vessels including Baltic Class, Arctic Class, and Canadian Arctic Class. Recently, the International Association of Classification Societies (IACS) has harmonized their classifications for Arctic vessels and has developed standards for seven Polar Class (PC) vessels. These vessel classes are not included in current Canadian regulations. Because of this, the Canadian government has investigated ways to update the Arctic shipping regulations to include the PC vessels by using a science-based approach. This paper presents an overview of the approach and highlights the results of the research to include the Polar Class vessels in the Canadian regulations.
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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 | 0.018 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".