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Record W7008221912

Assessing motion stress levels on board fishing vessels of the Newfoundland fleet

2004· dissertation· en· W7008221912 on OpenAlexaboutno aff

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2004
Typedissertation
Languageen
FieldEngineering
TopicShip Hydrodynamics and Maneuverability
Canadian institutionsnot available
Fundersnot available
KeywordsFishingSea trialOn boardBuoyShip motionsStress (linguistics)
DOInot available

Abstract

fetched live from OpenAlex

The aim of this study is to develop a numerical tool that could be used to assess the physical stress levels associated with the vessel motions on board fishing vessels of the Newfoundland fleet. A number of full-scale sea trials of typical fishing vessels of the fleet are underway to validate the numerical tool. Model experiments are expected to follow in the next year. If desirable correlations between the trials and the numerical results are obtained, simulations will be performed to obtain typical 'motion stress' profiles of various fishing boats over a typical fishing season. 'Motion Stress' will be quantified in terms of a parameter known as 'Motion Induced Interrupt' frequency (MII). Other parameters that can quantify the motion stress levels can also be incorporated into the numerical tool. Among them would be seasickness criteria and another postural stability index, which relates the motion levels on board to the 'motion stress'. -- For full-scale trials, two vessels will be used initially. In terms of their sizes, these represent one of the smallest vessels and one of the larger vessels of the fleet. During the trials a directional wave buoy was used to measure the wave characteristics. -- The Thesis will include the results from the full-scale sea trials and the numerical tool. Additionally, some sample results for motion stress levels will be presented.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.480
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0010.002
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 head0.029
GPT teacher head0.267
Teacher spread0.238 · 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.

Study designBench or experimental
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
Published2004
Admission routes1
Has abstractyes

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