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Record W2974060032 · doi:10.1017/s0373463319000705

Assessing Lifeboat Coxswain Training Alternatives Using a Simulator

2019· article· en· W2974060032 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.

Bibliographic record

VenueJournal of Navigation · 2019
Typearticle
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsTraining (meteorology)Simulation trainingCompetence (human resources)SimulationAeronauticsComputer scienceSubmarine pipelineEngineeringPsychology

Abstract

fetched live from OpenAlex

Lifeboats are essential life-saving equipment for all types of water-going vessels and offshore platforms. Lifeboat simulators have been created specifically for offshore personnel to practice in conditions that are normally too risky for live training. As simulation training is a relatively new alternative, there is a need to assess how training performed with a simulator compares with conventional training. This study was performed to evaluate how skills acquired with different training approaches transferred to an emergency scenario. Over a period of one year, participants received quarterly training in one of three ways: using live boats, computer-based training or a simulator. Following training, participants were evaluated on their ability to launch and manoeuvre a lifeboat in a plausible emergency. The study results suggest a benefit to performing training with realistic lifeboat controls and practicing using representative emergency scenarios. Insights are provided on how training can be modified to increase competence.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.384
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.118
GPT teacher head0.471
Teacher spread0.352 · 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