MétaCan
Menu
Back to cohort
Record W4301352336 · doi:10.5957/attc-2001-029

Real-time Estimation of Wave-induced Vertical Bending Moment from Ship Motion Measurements

2001· article· en· W4301352336 on OpenAlexaff
Jinsong Xu, M.R. Haddara

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsHullBending momentMoment (physics)Strain gaugeTime domainAcousticsBendingArtificial neural networkGeodesyGeologyComputer scienceStructural engineeringEngineeringMarine engineeringPhysicsArtificial intelligenceClassical mechanicsComputer vision

Abstract

fetched live from OpenAlex

A description is given of a time-domain technique for real-time estimation of a wave-induced vertical bending moment from ship motion measurements. The estimated values can be compared with those obtained from the strain gauge readings to ensure the validity of Hull Response Monitoring Systems. Artificial neural networks are used to model the time-domain relationship between wave-induced vertical bending moment and coupled heave and pitch motions. The proposed technique was validated using a set of experimental data.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.082
GPT teacher head0.313
Teacher spread0.231 · 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 designSimulation or modeling
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

Citations0
Published2001
Admission routes1
Has abstractyes

Explore more

Same topicStructural Health Monitoring TechniquesFrench-language works237,207