The Effect of Heavy Quarter Squats on Vertical Jump in Female Athletes
Bibliographic record
Abstract
The purpose of this study was to determine whether multiple, heavy quarter squats could acutely improve countermovement jump (CMJ) height through the mechanism of postactivation potentiation (PAP). Eleven female, collegiate volleyball and basketball athletes were recruited for this study. Forty-eight hours after determining their quarter squat one repetition max (1RM), the participants were brought back to the gymnasium and performed baseline CMJs, followed by the conditioning stimulus of five quarter squats at 90% of their 1RM. CMJs were then executed at 2 min, 4 min, and 6 min poststimulus. A comparison of the means showed increases between baseline jumps and poststimulus jumps of 0.59 in (1.50 cm), 0.46 in (1.17 cm), and 0.76 in (1.93 cm) at the 2 min, 4 min, and 6 min time points, respectively. The means demonstrated a slight improvement from pre to post, but a repeated measures ANOVA showed no significant difference between baseline and poststimulation CMJs, F(3,11) = 1.608, p = .262. The statistical power of the study was 28%. Future studies may need more participants to obtain greater power even though an a priori power analysis showed a minimum of 9 participants was needed to achieve 80% power. The results are encouraging, but the findings are not yet applicable to current training paradigms for college-age women. More research using this and other designs with female samples is necessary.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".