A method for evaluating operational implications of regulatory constraints on Arctic shipping
Bibliographic record
Abstract
Development of effective marine policy necessitates evidence-based, data-driven evaluations of the effects of regulatory constraints on operations. This is essential to better understand implications of policy decisions on complex socio-technical systems. This paper demonstrates a generalized methodology for evaluating operational implications associated with implementing maritime regulations. The method combines a ship performance model, regulatory constraint models, and multi-criteria pathfinding and optimization algorithms to evaluate and compare the operational implications of different regulatory constraints. The method is applied to Arctic shipping. The Polar Operational Limit Assessment Risk Indexing System (POLARIS) and the Arctic Ice Regimes Shipping System (AIRSS) are considered. POLARIS and AIRSS are regulatory guidelines used to assign structural safety constraints on ships in ice. Four approaches for assigning structural safety constraints are modelled: 1) POLARIS, 2) AIRSS, 3) speed limits established through a first-principles ship-ice interaction model, and 4) navigation in the absence of structural safety constraints. Operational implications are measured as distance, voyage time, and fuel consumption. Route optimization is validated against expert opinion of Arctic ship captains. Results indicate AIRSS is the more conservative regulatory guideline, yet associated with decreased voyage time and fuel consumption. Implications for marine policy and safe navigation are that, while POLARIS offers flexibility to operate in more severe ice conditions, it increases voyage time, fuel consumption, and the risk of vessel damage. Competent Arctic seafarers are critical for safe and efficient operations. The generalized methodology provides marine policy-makers and industry stakeholders with a means to evaluate operational implications of maritime 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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| 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".