Effect of Loading on Enhancement of Power Performance Over Three Consecutive Trials
Bibliographic record
Abstract
The acute effects of maximal voluntary isometric contractions (MVICs) in the squat position on subsequent measures of power output over 3 consecutive sets were investigated. Sixteen trained men experienced with back squats participated in the study. A 7-second MVIC was performed 4 minutes before the execution of 5 maximal countermovement jumps (CMJs) and was repeated for 3 consecutive sets (protocol 1). The results were compared to power output performance in a similar protocol (protocol 2) that excluded the 7-second MVICs. No significant differences occurred in any of the power output measurements between protocol 1 and protocol 2, nor did significance occur linearly across the 4 sets of CMJ, with the exception of a decrease in peak power in protocol 2 (p < or = 0.05). Using both mean and maximal values only one significant correlation between either relative strength and performance enhancement or absolute strength and performance enhancement was present at p < or = 0.01. At p < or = 0.05, significant correlations were found between absolute strength and mean peak power (PP), mean peak acceleration (PA), mean peak force (PF), max PP, max PA, max PF, and max peak velocity. These data indicate that the execution of an MVIC performed before a power exercise was inadequate to acutely enhance power output over any of 3 consecutive trials.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".