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Record W4226301078 · doi:10.1002/prs.12361

Comparison between simulation and conventional training: Expanding the concept of social fidelity

2022· article· en· W4226301078 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

VenueProcess Safety Progress · 2022
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsFidelityTraining (meteorology)Reliability (semiconductor)Set (abstract data type)Simulation trainingEngineeringHigh fidelitySimulationComputer scienceEngineering managementSystems engineering

Abstract

fetched live from OpenAlex

Abstract Daily operations onboard ships are very challenging due to man–machine interactions. To improve daily operational safety and to prevent losses due to machinery breakdown, effective risk management techniques need to be developed, considering various operational and environmental factors affecting the seafarers' performance. The current study explains the comparison between simulation and conventional classroom training to enhance safety in maritime operations in compromised environments. The contribution of this study lies in introducing the concept of social fidelity in simulator‐based training. This study bridges the gap between computer technology and collaborative learning activities in simulator‐based training. The result obtained through the simulation improves marine engineers' training and enhances the reliability of marine engines. This paper concludes by proposing a set of recommendations for the future design of simulator‐based training for marine engineers.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.507

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 studies0.0010.000
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 head0.124
GPT teacher head0.466
Teacher spread0.342 · 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