The costs of icebreaking services: an estimation based on Swedish data
Bibliographic record
Abstract
Abstract In winter, the sea around Sweden and Finland as well as parts of the waters around Canada, Russia and the USA become ice covered, and ships may require assistance from icebreakers to proceed to their destinations. This paper accordingly analyses the cost structure and estimates the cost of icebreaking operations at sea, including the costs of external effects of the icebreakers’ emissions, and analyses the consequences of different pricing schemes for financing icebreaking services. A regression analysis was carried out based on data from icebreaking services in Sweden over 14 winters from 2001/2002 to 2015/2016. The social marginal cost of an average assistance operation (which may involve more than one ship) is estimated at EUR 6476 and for each assisted ship EUR 5304. The same cost is EUR 907 per running hour for the icebreakers and EUR 1990 per hour a ship is assisted. Each additional nautical mile sailed by an icebreaker costs society EUR 141 and each assisted nautical mile EUR 234. The marginal cost is found not to be related to winter severity. Despite the significant social marginal costs, not including large fixed costs, icebreaking in Sweden and Finland is free of charge. The advantages and disadvantages of four pricing models that can be applied to cover at least parts of the costs to society are discussed. All models could create new distortions, but a price per assisted hour may be worth applying in practice.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".