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Record W2031348704 · doi:10.1080/00140130701295947

Predictors of perceived effort in the shoulder during load transfer tasks

2007· article· en· W2031348704 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

VenueErgonomics · 2007
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of Waterloo
FundersAutomotive Research Center
KeywordsPsychologyHuman factors and ergonomicsApplied psychologyComputer sciencePoison controlEngineeringMedicineMedical emergency

Abstract

fetched live from OpenAlex

The mechanism of muscular effort perception in the shoulder was examined in this experiment. Two shoulder biomechanical models and experimental muscle activity data were used to assess physical exposure for a series of reaching tasks. Effort perception was quantitatively correlated to these measures of physical loading, both at the resultant torque (r(2) = 0.50) and muscle activity model-based muscle force predictions (MFPs): r(2) = 0.42, electromyography (EMG): r(2) = 0.26) levels. Muscle data did not explain variation in effort perception more fully than torque data. The inclusion of subject and task variables improved the ability of each model to explain variability in effort perception (torque: r(2) = 0.74; MFP: r(2) = 0.67, EMG: r(2) = 0.64). These results suggest that effort perception may not be fully explained by only an image of the motor command, but is rather a complex integrative quantity that is affected by other factors, such as posture and task goals, which may be dependent on sensory feedback.

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 score0.178
Threshold uncertainty score0.210

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 head0.010
GPT teacher head0.262
Teacher spread0.252 · 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