Modeling Emissions of Shipping Operations in Sea Ice
Bibliographic record
Abstract
Abstract The marine industry is a large contributor to overall global emissions and must mitigate its environmental impacts through innovation. These improvements can be achieved through combinations of design fundamentals, such as more efficient hull forms, technical innovations, such as wind-assisted propulsion, and operational transformations, such as switching to alternative fuels. Adopting various decarbonization measures will be expensive and must be informed by a detailed understanding of ship performance. In this paper, a method to predict carbon emissions is presented and applied to the case of a medium icebreaker. The emissions predictions are based on a model of ship resistance and propulsion in ice and open water, and they constitute an essential part of a general ship performance model. Route optimization can be informed by such ship performance models in combination with routing algorithms and rewards functions. Our goal is to incorporate a detailed emissions prediction method in a route optimization framework explicitly aimed at transits involving sea ice. The paper includes a description of a full-scale field trial through which we evaluate the accuracy of the predictions and develop a profile of emissions for tactical maneuvers to broaden the prediction capabilities. The results of the trials will be presented at the OMAE conference in Singapore.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".