Volume, intensity and timing of muscle power potentiation are variable
Bibliographic record
Abstract
Differences in peak times were all trivial or small (i.e. d<0.50), though all comparisons with peak force (6.57 min±5.33) were large (d>0.80), which is to say the peak force reached its peak substantially later than the other dependent variables. In assessing just the peak data, magnitude-based inference revealed both the 5x70 and 3x85 protocol elicited changes that exceed 75% likelihood of exceeding the smallest worthwhile change (SWC) for peak power (89% and 80% likely respectively) and velocity (77% and 87% likely respectively). The 10x70 also had a substantial likelihood of potentiating peak velocity above the SWC (75% likely). The 5x70 protocol had an 80% likelihood of exceeding the SWC in mean power. Discussion Most measures peaked at one-, three-, or five-minutes though this peak was often not greater than the smallest worthwhile change. There was sufficient inconsistency in the timing of the peak that there was no statistically significant potentiation in the repeated measures ANOVA. Magnitude-based inferences revealed that while no protocol had a substantial likelihood of potentiating the peak vertical jump above the SWC, the 5x70 protocol had the most consistent substantial likelihood (i.e. >75%) of increasing the peak of most dependent variables, in particular power as well as peak velocity. We were unable to consistently predict if these peaks occurred at 1-, 3, or 5 minutes post-stimulation though declines after 5 min seems probable.Volume, intensity and timing of muscle power potentiation are variable Behm, D.G.1, Chaouachi, A.2, Abed, M.F.2, Poulos, N.3, Drinkwater, E.J.4 1: Newfoundland, Canada, 2: Tunis Tunisia, 3. Aspire, Qatar, 4. Bathurst Australia Introduction Whereas muscle potentiation is consistently demonstrated with evoked contractile properties, the potentiation of functional and physiological measures is more inconsistent. The objective of the study was to compare a variety of conditioning stimuli volumes and intensities over a 15-minute
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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 | 0.002 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".