A Sensitivity Analysis of the Voyage Adjustment for Sailing in Ice of the Carbon Intensity Indicator Regulation
Bibliographic record
Abstract
Abstract The Carbon Intensity Indicator (CII) regulation came into force in 2023 with the objective of reducing greenhouse gas emissions in the maritime industry. Ice-classed vessels sailing in ice have been granted a voyage adjustment in the CII regulation. The voyage adjustment allows ice-classed vessels to exclude from the calculation of CII the part of any voyage that constitutes sailing in ice. The current study examines a sensitivity analysis of definitions for sailing in ice conditions. Sailing in ice is defined in the current regulation as an ice-classed vessel operating in a sea area within the ice edge. Alternative definitions have been considered which are more specific in terms of concentration and thickness of the ice field where the vessel operates. We present a case study of a conventional diesel-powered bulk carrier with ice class IA Super on a voyage in the Canadian Arctic. Routing of the vessel is optimized based on an assumed rewards function, a routing algorithm, and a ship performance model. We compare the routes selected under different definitions of “sailing in ice conditions”. The results show that different definitions of sailing in ice conditions lead to different optimal routes for the vessel, some of which are more effective than others in meeting the IMO’s strategic objective of reducing emissions.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".