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Record W4297932542 · doi:10.5957/icetech-2014-148

Literature Survey of Station Keeping and Ice Management

2014· article· en· W4297932542 on OpenAlexaff
Ivana Kubat, Mohamed Sayed

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsComputer scienceOffset (computer science)Submarine pipelineThe arcticLead (geology)Systems engineeringRisk analysis (engineering)Data scienceRemote sensingEngineeringOceanographyGeologyBusiness

Abstract

fetched live from OpenAlex

Station keeping of drillships in ice has emerged as a key enabling factor for offshore developments in intermediate and deep Arctic waters. A drillship would have to keep position within strict offset limits for safe operations. This requires reliable information concerning levels of ice management, estimates of ice forces, performance of mooring and Dynamic Positioning systems, and the corresponding responses of the vessels. In spite of the importance of these issues, several technical aspects remain poorly understood. This has prompted substantial recent research efforts. The objective of the present review is to provide the reader with efficient and thorough up-to-date information on available literature which deals with the various aspects of stationkeeping in ice. The review includes compilation of the literature and analysis that categorizes the investigations according to their objectives, approaches, scopes and outputs.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.013
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0130.021
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.010
GPT teacher head0.200
Teacher spread0.191 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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

Citations0
Published2014
Admission routes1
Has abstractyes

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