Volume, intensity, and timing of muscle power potentiation are variable
Bibliographic record
Abstract
Whereas muscle potentiation is consistently demonstrated with evoked contractile properties, the potentiation of functional and physiological measures is inconsistent. The objective was to compare a variety of conditioning stimuli volumes and intensities over a 15-min recovery period. Twelve volleyball players were subjected to conditioning stimuli that included 10 repetitions of half squats with 70% of 1-repetition maximum (RM) (10 × 70), 5 × 70, 5 × 85, 3 × 85, 3 × 90, 1 × 90, and control. Jump height, power, velocity, and force were measured at baseline, 1, 3, 5, 10, and 15 min. Data were analysed with a 2-way repeated measure ANOVA and magnitude-based inferences. The ANOVA indicated significant decreases in jump height, power, and velocity during recovery. This should not be interpreted that no potentiation occurred. Each dependent variable reached a peak at a slightly different time: peak jump height (2.8 ± 2.3 min), mean power (3.6 ± 3.01 min), peak power (2.5 ± 1.8 min), and peak velocity (2.5 ± 1.8 min). Magnitude-based inference revealed that both the 5 × 70 and 3 × 85 protocol elicited changes that exceeded 75% likelihood of exceeding the smallest worthwhile change (SWC) for peak power and velocity. The 10 × 70 and the 5 × 70 had a substantial likelihood of potentiating peak velocity and mean power above the SWC, respectively. Magnitude-based inferences revealed that while no protocol had a substantial likelihood of potentiating the peak vertical jump, the 5 × 70 had the most consistent substantial likelihood of increasing the peak of most dependent variables. We were unable to consistently predict if these peaks occurred at 1, 3, or 5 min poststimulation, though declines after 5 min seems probable.
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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 | 0.001 | 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) | 0.002 | 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".