Muscle fiber composition, jumping performance, and rate of force development adaptations induced by different power training volumes in females
Bibliographic record
Abstract
This study aimed to investigate the effect of 3 different eccentric-only power training volumes on muscle fiber type composition and power performance. Twenty-nine females were assigned into 3 groups and performed 10 weeks of either 3 (low volume), 6 (moderate volume), or 9 (high volume) sets/session of 4 fast-velocity eccentric-only half-squats against 70% of concentric 1-repetition maximum (1RM), followed by 3 maximum countermovement jumps (CMJs) after each set. Half-squat 1RM, CMJ height/power, maximum isometric force, rate of force development (RFD) and muscle fiber cross-sectional area (CSA) were increased in all groups (p = 0.001). Low-volume training induced higher increases in CMJ height/power and early RFD, compared with the moderate- and high-volume training programs (p < 0.001). Significant reductions in type IIx muscle fiber percentages and %CSAs were found after moderate- and high-volume training, with concomitant increases in type IIa fibers (p = 0.001). Significant correlations were found between the changes in type IIa and type IIx percentages, fiber CSA, %CSA, and the changes in performance (r: –0.787 to 0.792; p < 0.05). These results suggest that relatively large eccentric power training volumes may result in detrimental neuromuscular adaptations, minimal changes in early RFD, and a reduction of type IIx muscle fiber percentage. Novelty Low but not high volume of power training maintains type IIx muscle fibers. Early rate of force development increases after a low- or moderate-power training volume, but not after a high-power training volume. Training-induced changes in type IIx muscle fiber percentage is related with changes in early rate of force development.
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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 | 0.000 |
| 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) | 0.002 | 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".