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Record W4297259537 · doi:10.1123/mc.2021-0114

Uncontrolled Manifold Analysis of the Effects of Different Fatigue Locations on Kinematic Coordination During a Repetitive Upper-Limb Task

2022· article· en· W4297259537 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

VenueMotor Control · 2022
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsKinematicsTrunkPhysical medicine and rehabilitationTask (project management)ElbowUpper limbPosition (finance)Physical therapyMathematicsPsychologyMedicineEngineeringAnatomyPhysicsBiology

Abstract

fetched live from OpenAlex

Fatigue at individual joints is known to affect interjoint coordination during repetitive multijoint tasks. However, how these coordination adjustments affect overall task stability is unknown. Twelve participants completed a repetitive pointing task at rest and after fatigue of the shoulder, elbow, and trunk. Upper-limb and trunk kinematics were collected. Uncontrolled manifold framework was applied to a kinematic model to link elemental variables to endpoint fingertip position. Mixed and one-way analysis of variances determined effects (phase and fatigue location) on variance components and synergy index, respectively. The shoulder fatigue condition had the greatest impact in causing increases in variance components and a decreased synergy index in the late phase of movement, suggesting more destabilization of the interjoint task caused by shoulder fatigue.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.879
Threshold uncertainty score0.424

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.004
GPT teacher head0.186
Teacher spread0.183 · 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