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Record W6945386513 · doi:10.25384/sage.c.5123361

The Effect of Wave Motion Intensities on Performance in a Simulated Search and Rescue Task and the Concurrent Demands of Maintaining Balance

2020· other· en· W6945386513 on OpenAlexaff

Bibliographic record

VenueSage Journals Data · 2020
Typeother
Languageen
FieldComputer Science
TopicResearch Data Management Practices
Canadian institutionsSimon Fraser UniversityUniversity of British Columbia
Fundersnot available
KeywordsTask (project management)Motion (physics)Balance (ability)Search and rescueElectromyographyIntensity (physics)Motion captureControl (management)

Abstract

fetched live from OpenAlex

ObjectiveThe purpose of this study was to examine how intensity of wave motions affects the performance of a simulated maritime search and rescue (SAR) task.BackgroundMaritime SAR is a critical maritime occupation; however, the effect of wave motion intensity on worker performance is unknown.MethodsTwenty-four participants (12 male, 12 female) performed a simulated search and rescue task on a six-degree-of-freedom motion platform in two conditions that differed in motion intensity (low and high). Task performance, electromyography (EMG), and number of compensatory steps taken by the individual were examined.ResultsAs magnitude of simulated motion increased, performance in the SAR task decreased, and was accompanied by increases in lower limb muscle activation and number of steps taken.ConclusionsPerformance of an SAR task and balance control may be impeded by high-magnitude vessel motions.ApplicationThis research has the potential to be used by maritime engineers, occupational health and safety professionals, and ergonomists to improve worker safety and performance for SAR operators.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
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)0.0040.000

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 head0.078
GPT teacher head0.358
Teacher spread0.280 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2020
Admission routes1
Has abstractyes

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