Muscle fibre recruitment can respond to the mechanics of the muscle contraction
Bibliographic record
Abstract
This study investigates the motor unit recruitment patterns between and within muscles of the triceps surae during cycling on a stationary ergometer at a range of pedal speeds and resistances. Muscle activity was measured from the soleus (SOL), medial gastrocnemius (MG) and lateral gastrocnemius (LG) using surface electromyography (EMG) and quantified using wavelet and principal component analysis. Muscle fascicle strain rates were quantified using ultrasonography, and the muscle-tendon unit lengths were calculated from the segmental kinematics. The EMG intensities showed that the body uses the SOL relatively more for the higher-force, lower-velocity contractions than the MG and LG. The EMG spectra showed a shift to higher frequencies at faster muscle fascicle strain rates for MG: these shifts were independent of the level of muscle activity, the locomotor load and the muscle fascicle strain. These results indicated that a selective recruitment of the faster motor units occurred within the MG muscle in response to the increasing muscle fascicle strain rates. This preferential recruitment of the faster fibres for the faster tasks indicates that in some circumstances motor unit recruitment during locomotion can match the contractile properties of the muscle fibres to the mechanical demands of the contraction.
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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 | 0.000 |
| 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".