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Record W821025700 · doi:10.5957/mt1.2008.45.2.68

Robust Statistical Methods for Analysis of Ship Motion Simulation Results

2008· article· en· W821025700 on OpenAlex
D. Perrault

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.

Bibliographic record

VenueMarine Technology and SNAME News · 2008
Typearticle
Languageen
FieldEngineering
TopicShip Hydrodynamics and Maneuverability
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsReliability (semiconductor)Process (computing)Interval (graph theory)Key (lock)Computer scienceExtension (predicate logic)Chebyshev filterReliability engineeringEngineeringIndustrial engineeringMathematics

Abstract

fetched live from OpenAlex

Simulation of ship motion is becoming increasingly more viable as a method for estimating the value of parameters that can be used for design, life-cycle management, risk analysis, developing and validating operational tactics, training, etc. It is vitally important to be able to draw sound conclusions from the simulation results. An examination of the process for obtaining parameter estimates and the means of determining their reliability is key to evaluating the confidence that can be placed in the results and avoiding drawing inappropriate conclusions from the simulation results. An extension of Chebyshev's Theorem was developed to provide a robust general tool to provide a conservative estimate of the probability that the parameter is within a specific interval. In addition, a method for determining the number of simulation runs required is suggested.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.516
Threshold uncertainty score0.345

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.041
GPT teacher head0.315
Teacher spread0.274 · 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