Experience influences kinematic motor synergies: an Uncontrolled manifold approach to simulated Nordic skiing
Bibliographic record
Abstract
Motor synergies are defined as central nervous system mechanisms which adjust participating degrees of freedom to ensure dynamic stability (control) of certain performance variables and have been identified during many motor tasks. The potential for synergistic control of individual segments during full-body tasks is often overlooked. Thus, this study compared individual differences in the potential stabilization of multiple performance variables on the basis of experience during a full-body sport activity. Normalized time series of synergy indices from Uncontrolled Manifold analyses on experienced (n = 9) and inexperienced (n = 19) participants were analysed using statistical parametric mapping during simulated Nordic skiing. Regardless of experience, hand, upper arm, and whole-body centre of mass (COM) kinematics were found to be stabilized by kinematic motor synergies. Only experienced Nordic skiers stabilized trunk COM position at all, while trunk COM velocity was stabilized for a longer duration than inexperienced participants. However, inexperienced participants stabilized hand velocity for a greater duration overall and to a greater magnitude during early pull phase than the experienced skiers. That motor synergies for hand and trunk COM velocity differed between experience groups suggests potential utility for these performance variables as indicators of motor skill development for full-body tasks such as Nordic skiing.
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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 | 0.000 | 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 | 0.001 |
| 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".