A goal-based approach for selecting a ship's polar class
Bibliographic record
Abstract
Following the International Code for Ships Operating in Polar Waters (Polar Code), ships operating in ice-covered polar waters must comply with an appropriate Polar Class (PC) or equivalent ice class standard. For the selection of an appropriate Polar Class, ship designers and operators are encouraged to use the Polar Operational Limit Assessment Risk Indexing System (POLARIS). A limitation of POLARIS is that it does not consider the extent to which a ship operates in various ice conditions, and thus also not the probabilistic nature of ice loading. To address this limitation, this article outlines a goal-based approach that is intended to complement POLARIS when selecting a ship's Polar Class. Following the proposed approach, the appropriateness of a ship's minimum required Polar Class as determined using POLARIS is evaluated by assessing the ship's long-term extreme ice loads, and by relating these to the design loads behind the considered Polar Class standard. To account for the probabilistic nature of ice loading, the approach calculates a ship's long-term extreme ice loads considering its intended operating profile and expected ice exposure. This is achieved by synthesising a modified version of the so-called event-maximum method, discrete-event simulations, and satellite ice data. The utility of the proposed approach is demonstrated through a case study, in which it is used as a complement to POLARIS to select an appropriate Polar Class for a double-acting ship intended for year-round independent operations along the northeast coast of Canada.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".