Functional specificity and hierarchical control of trunk muscles in complex rhythmic movements
Bibliographic record
Abstract
Belly dance was used as a novel paradigm for exploring motor control of voluntary trunk movements.Motion capture and electromyography were combined to record segmental motion and muscle activation patterns.A variety of different trunk isolation movements varying in tempo, range of motion and pelvic movement trajectories, were recorded to explore how the human nervous system controls complex, segmentally specific rhythmic trunk motion.Results from the first study confirmed predictions from anatomical studies that different portions of the lumbar spine extensors could have independent functions, and established that belly dance could serve as a useful paradigm for exploring paraspinal muscle function and neural muscular specification in segmental spine motion.In the second study the previous findings on compartmental control of lumbar spine extensor muscles were extended to explore how muscle activation timing changes with tempo and training.Three different patterns were observed based on this capability for neuromuscular specificity of paraspinal muscles at different vertebral or segmental levels.The activation patterns resembled those corresponding to different gaits or locomotor modes observed in lamprey, salamander, humans and other mammals.Pattern selection and switching were dependent on task, training and tempo, with an interaction amongst these factors.Differences between novice and trained performers were seen in the selection of different patterns at low frequencies, but convergence toward a common pattern in higher frequency oscillations, findings which supported the proposal that the lower frequency variations are under voluntary descending control while the higher frequency versions involve lower, more automatic levels of control.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".