MétaCan
Menu
Back to cohort
Record W3190505286 · doi:10.5750/ijme.v163ia1.12

Vessel Motions and Work Interruptions Aboard a Fast Rescue Craft

2021· article· en· W3190505286 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

VenueThe International Journal of Maritime Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicShip Hydrodynamics and Maneuverability
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsCoast guardHullStatistical analysisAccelerationMarine engineeringSea trialAeronauticsGeodesyMeteorologyGeologyEngineeringStatisticsPhysicsMathematics

Abstract

fetched live from OpenAlex

A set of field trials were carried out aboard a Canadian Coast Guard fast rescue Rigid Hull Inflatable Boat. The vessel was outfitted with a data acquisition system to collect vessel and engine performance data and trialled in three wave conditions (approx. Beaufort 2 to 7). This paper focusses on the methodologies and results for calculating and investigating Motion-Induced Interruptions (MIIs). MIIs due to lateral and longitudinal overbalancing and sliding were investigated using the counting of motion events which are expected to cause an interruption, supported by a statistical analysis and examination of the distribution of the MII data. We conclude that MII assessments of small, light, high-speed craft such as the one studied should include longitudinal acceleration and pitch angle, typically assumed to be non-influential in MII assessments. Statistical treatments have promise for analysis of field-acquired MII 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.

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: Empirical
Teacher disagreement score0.162
Threshold uncertainty score0.360

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.007
GPT teacher head0.214
Teacher spread0.206 · 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