Extrapolation of an empirical elbow muscle co-activation relationship to a novel task set: implications for predictions of individual muscle demands
Bibliographic record
Abstract
Biomechanical optimisation models applying efficiency-based objective functions often underestimate antagonist contributions. Previous work has quantified an empirical co-activation relationship in the elbow musculature, demonstrating that implementing this relationship as a constraint in an elbow muscle force prediction model improves muscle force predictions. The current study evaluated this modified model by extrapolating the co-activation relationship to 36 novel isometric unilateral, right-handed exertions, including those requiring greater intensity of effort and performed in different postures. Surface electromyography was recorded from the elbow flexors and extensors. Novel extrapolative co-activation relationships were developed and used as constraints in a muscle force prediction model. Model predictions using both constraints were compared with empirical biophysical data. Predictions by the modified model were more consistent with biophysical data than those by the original model for the novel exertions. Novel co-activation relationships did not further enhance predictions when compared with the previous relationship, suggesting that extrapolation of the previous relationship is feasible.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".