Investigating the role of the shoulder musculature during maximum unilateral isometric exertions
Bibliographic record
Abstract
The extent to which specific muscles may limit maximum isometric force production is largely unknown. This study investigated shoulder muscle activity in six muscles and maximum force generation at the hand in three directions, while in eight different working positions. Ten right hand dominant, university-aged female participants completed twenty-four maximal isometric force hand exertions against a handle positioned by a robot arm within a 3-dimensional simulated workspace. A multivariate, full factorial ANOVA indicated a reach distance main effect where 11% greater force production occurred at the lesser reach distance. A target handle elevation and force direction interaction effect on maximum force production also existed. These findings add to normative static strength data for hand locations typical of an operating work envelope. Although hand force was significantly influenced by position in each direction (p < 0.05), muscle EMG was not influenced in any of the six muscles measured. No muscle achieved 100% MVE in any of the tested conditions, with the highest total muscle activity recorded at 86% MVE for the Pectoralis Major – sternal origin. Collectively, these data better demonstrate that the location and direction of work presentation influences force outputs more than specific muscle demands (of those measured), and should be considered in evaluating workstations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".