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Record W2121053811 · doi:10.1080/00140139.2015.1026405

Simulated motion negatively affects motor task but not neuromuscular performance

2015· article· en· W2121053811 on OpenAlexafffund
Gregory E. P. Pearcey, Scott N. MacKinnon, Duane C. Button

Bibliographic record

VenueErgonomics · 2015
Typearticle
Languageen
FieldEngineering
TopicMaritime Navigation and Safety
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaAtlantic Canada Opportunities Agency
KeywordsPhysical medicine and rehabilitationTask (project management)Motion (physics)ElectromyographyComputer scienceSimulationMedicineEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

The effects of long duration simulated motion on motor task and neuromuscular performance along with time frames required to recover from these effects are relatively unknown. This study aimed to determine (1) how simulated motion affects motor task and neuromuscular performance over one hour of motion and (2) the time course of recovery from any decrements. The dependent variables that were measured included: reaction time; visuomotor accuracy tracking; maximal voluntary contractions; voluntary activation; evoked contractile properties and biceps brachii electromyography of the elbow flexors. Reaction times and error rates of the visuomotor accuracy tracking task were compromised in motion, but maximal force, voluntary activation, evoked contractile properties and rmsEMG responses of the biceps brachii were unaffected by motion. It is concluded that motion causes an increase in attention demands, which have a greater effect on motor task rather than neuromuscular performance. PRACTITIONER SUMMARY: Minor delays or mistakes can separate life and death at sea. The safety and productivity of most vessels rely on error-free performance of motor tasks. This study demonstrates that human ability to perform motor tasks is compromised by ship motions and may aid in developing training and safety guidelines for seafarers.

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)0.000
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 score0.022
Threshold uncertainty score0.597

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 head0.016
GPT teacher head0.202
Teacher spread0.186 · 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.

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

Citations3
Published2015
Admission routes2
Has abstractyes

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