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Record W2999489503 · doi:10.1007/s13437-019-00191-x

The costs of icebreaking services: an estimation based on Swedish data

2020· article· en· W2999489503 on OpenAlexaboutno aff
Eva Lindborg, Peter Andersson

Bibliographic record

VenueWMU Journal of Maritime Affairs · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
FundersLinköpings Universitet
KeywordsNautical mileMarginal costMileCost estimateEngineeringTransport engineeringOperations researchEconomicsGeography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.973
Threshold uncertainty score0.405

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.043
GPT teacher head0.330
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2020
Admission routes1
Has abstractyes

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