Scenario-Based Risk Management for Arctic Waters
Bibliographic record
Abstract
Abstract Arctic shipping is growing driven by a demand for natural resources, climate change, and technological development, among other factors. While this provides many benefits for society, it also entails risks for people, the environment, and property. The purpose of this article is to assist ship designers, operators, owners, and other stakeholders in managing those risks by defining a comprehensive approach to scenario-based risk management for Arctic waters. The approach covers both the management of short-term operational risks, as well as of risks related to a ship’s long-term extreme (design) ice loads and structural response. For operational risk management, a further developed version of the established Polar Operational Limitations Assessment Risk Indexing System (POLARIS) method is defined. In contrast to the established method, the further developed version considers the consequences of potential accidental events. For managing risks related to a ship’s long-term extreme ice loads and structural response, guidelines are provided for the application of existing methods of assessing ice loads, including analytical, numerical, and semi-empirical methods. In addition, to support the design of ice class ship structures, a new approach based on closed-form expressions is defined that can be used in the conceptual design phase to determine preliminary scantlings of primary hull structural members (e.g., transverse web frames).
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".