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Record W1989555140 · doi:10.1115/omae2014-23421

Transportation Risk Analysis Framework for Arctic Waters

2014· article· en· W1989555140 on OpenAlexaff
Faisal Khan, Ming Yang, Brian Veitch, Sören Ehlers, Shuhong Chai

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldDecision Sciences
TopicRisk and Safety Analysis
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsArcticContingency planMarine transportationThe arcticEnvironmental scienceSea iceClimate changeWork (physics)Environmental resource managementEnvironmental planningOceanographyComputer scienceTransport engineeringEngineeringGeology

Abstract

fetched live from OpenAlex

Arctic waters have historically been relatively inaccessible for marine transport. Lately, climate change has made more of this region ice-free in the summer season. This has reduced the difficulty of marine transport in Arctic waters. Further, exploration and development of natural resources is increasing in Arctic regions, as is destinational shipping. The unique risk factors of this region, such as extremely low temperature, ice conditions and drifting icebergs, continue to pose threats to transportation. Potential impacts associated with marine transportation accidents warrant contingency plans that recognize that preventative measures may fail. To plan effectively, a transportation accident risk assessment model for Arctic waters is helpful. There is limited work on the development of such models. A new cause-consequences based risk assessment model is proposed here. The model estimates the probability of a transportation accident and also the related consequences during navigation in Arctic waters. To illustrate the application of the methodology, it is applied to a case of an oil-tanker collision on the Northern Sea Route.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.817
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.049
GPT teacher head0.370
Teacher spread0.321 · 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 designObservational
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

Citations23
Published2014
Admission routes1
Has abstractyes

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