Predictors of perceived effort in the shoulder during load transfer tasks
Bibliographic record
Abstract
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.
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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 | 0.010 |
| 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 | 0.001 | 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".