Motor unit recruitment patterns 1: responses to changes in locomotor velocity and incline
Bibliographic record
Abstract
Mammalian skeletal muscles are composed of a mixture of motor unit types, which contribute a range of mechanical and physiological properties to the muscle. For a muscle to effectively contribute to smooth, co-ordinated movement it must activate an appropriate number and combination of motor units to generate the required force over a suitable time period. Much evidence exists indicating that motor units are activated in an orderly fashion, from the slowest through to the fastest. A growing body of evidence, however, indicates that such a recruitment strategy does not always hold true. Here we investigate how motor unit recruitment patterns were influenced by changes in locomotor velocity and incline. Kinematics data and myoelectric signals were collected from three rat ankle extensor muscles during running on a treadmill at nine velocity and incline combinations. Wavelet and principal component analysis were used to simultaneously decompose the signals into time and frequency space. The relative frequency components of the signals were quantified during 20 time windows of a stride from each locomotor condition. Differences in signal frequency components existed between muscles and locomotor conditions. Faster locomotor velocities led to a relative increase in high frequency components, whereas greater inclines led to a relative increase in the low frequency components. These data were interpreted as representing changes in motor unit recruitment patterns in response to changes in the locomotor demand. Motor units were not always recruited in an orderly manner, indicating that recruitment is a multi-factorial phenomenon that is not yet fully understood.
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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 | 0.000 | 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".