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Record W3080815577 · doi:10.5957/josr.05190024

Probability of Sea Condition for Ship Strength, Stability, and Motion Studies

2021· article· en· W3080815577 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Ship Research · 2021
Typearticle
Languageen
FieldEngineering
TopicShip Hydrodynamics and Maneuverability
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsContext (archaeology)FidelityMarine engineeringWind waveMeteorologyComputer scienceScope (computer science)Forcing (mathematics)Environmental scienceOperations researchEngineeringGeologyTelecommunicationsOceanographyGeographyClimatology

Abstract

fetched live from OpenAlex

Modeling and simulation continues to be an important tool for determining the response of sea-going vessels to wind and waves. To provide appropriate forcing functions to the models, it is important to have environmental data of sufficient fidelity to facilitate an assessment of platform response, which is as accurate as possible within the practical constraints of time and resources. Fortunately, there are a variety of sources of good wave data, including the U.S. National Oceanic and Atmospheric Administration. This study examines the wave data in the context of simulation codes for assessing characteristics of ocean craft response. It also looks at some practical considerations to limit the scope of simulations. The work is strongly influenced by modeling and simulation of naval surface ships, looking for extreme behaviors, but many of the issues discussed are broadly applicable to other applications. Copyright 2021 Her Majesty the Queen in Right of Canada, Department of National Defence

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.

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)0.004
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: Empirical
Teacher disagreement score0.413
Threshold uncertainty score0.318

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.203
GPT teacher head0.397
Teacher spread0.194 · 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