Early deactivation of slower muscle fibres at high movement frequencies
Bibliographic record
Abstract
Animals produce rapid movements using fast cyclical muscle contractions. These types of movements are better suited to faster muscle fibres within muscles of mixed fibre types as they can shorten at faster velocities and achieve higher activation-deactivation rates than their slower counterparts. Preferential recruitment of faster muscle fibres has previously been shown during high velocity contractions. Additionally, muscle deactivation takes longer than activation and therefore may pose a limitation to fast cyclical contractions. It has been speculated that slower fibres may be deactivated before faster fibres to accommodate their longer deactivation time. This study aimed to test whether shifts in muscle fibre recruitment occur with derecruitment of slow fibres before faster fibres at high cycle frequencies. Electromyographic (EMG) signals were collected from the medial gastrocnemius at an extreme range of cycle frequencies and workloads. Wavelets were used to resolve the EMG signals into time and frequency space and the primary sources of variability within the EMG frequency spectra were identified through principal component analysis. Early derecruitment of slower fibres was evident at the end of muscle excitation at higher cycle frequencies, as determined by reduced low-frequency EMG content, and additional slower fibre recruitment was present at the highest cycle frequency. The duration of muscle excitation reached a minimum of about 150 ms and did not change for the three highest cycle frequencies, suggesting a duration limit for the medial gastrocnemius. This study provides further evidence of modifications of muscle fibre recruitment strategies to meet the mechanical demands of movement.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".