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Record W2136083960 · doi:10.1123/jab.24.2.103

Manual Materials Handling in Simulated Motion Environments

2008· article· en· W2136083960 on OpenAlexafffund
Michael W.R. Holmes, Scott N. MacKinnon, Julie Matthews, Wayne J. Albert, Steven Mills

Bibliographic record

VenueJournal of Applied Biomechanics · 2008
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of New BrunswickMemorial University of NewfoundlandMcMaster University
FundersCanadian Institutes of Health Research
KeywordsKinematicsSagittal planeTrunkTorsoMotion (physics)Physical medicine and rehabilitationBarefootDeckMotion analysisSimulationMaterial handlingMathematicsComputer scienceMedicineStructural engineeringEngineeringArtificial intelligencePhysicsAnatomy

Abstract

fetched live from OpenAlex

Seafaring occupations have been shown to place operators at an increased risk for injury. The purpose of this study was to understand better the demands of a moving environment on the ability of a person to perform specific lifting tasks. Subjects lifted a 15-kg load under four different lifting conditions. A 6-degree-of-freedom ship motion simulator imposed repeatable deck motions under foot while subjects executed the lifting tasks. Subjects were oriented in three different positions on the simulator floor to inflict different motion profiles. Electromyographic records of four muscles were collected bilaterally, and thoracolumbar kinematics were measured. A repeated-measures ANOVA was employed to assess trunk motions and muscle activities across lifting and motion conditions. The erector spinae muscles showed a trend toward significant differences for motion effects. Maximal sagittal velocities were significantly smaller for all motion states in comparison with the stable condition (p <or= .01), whereas maximum twisting and lateral bending velocities were higher (p <or= .05). Results suggest working in a moving environment will likely increase the operator's risk for overexertion injuries, particularly to the trunk region.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.295

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 communication0.0000.000
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 head0.013
GPT teacher head0.256
Teacher spread0.243 · 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 designBench or experimental
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

Citations18
Published2008
Admission routes2
Has abstractyes

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