Analysis of vertical jump, rating of perceived exertion, delayed-onset muscle soreness, and muscular peak power in young male Brazilian football players submitted to plyometric and semi-squat training with weights
Bibliographic record
Abstract
This study analyzed the effects of plyometric and strength training on vertical jump (VJ), rating of perceived exertion (RPE), delayed-onset muscle soreness (DOMS), and absolute (APP) and relative (RPP) muscle peak power in young male football players. Twenty-five participants were randomly divided into semi-squat training group (SSTG), plyometric training group (PTG), and control group (CG). The duration of the intervention was six weeks. VJ was analyzed with a computerized jumping platform. DOMS and RPE with the Borg’s Visual Analogue Scale (VAS) and the Adapted Borg Scale (ABS), respectively. The SSTG showed improvements (p < 0.05) in countermovement jump (CMJ), squat jump (SJ), APP (3190.67 ± 338.49 W), and RPP (47.75 ± 5.01 W/kg). PTG showed improvements (p < 0.05) in SJ. In the intragroup comparations, SSTG, PTG, and CG showed an increase (p < 0.05) in RPE and DOMS. Between groups, PTG presented an increase (p < 0.05) on RPE and DOMS compared with SSTG and CG. CMJ presented strong correlations between APP and VJ, RPP, and VJ, and APP and RPP. SJ showed a higher positive correlation between all the physical variables. Only SSTG promoted an increase in both types of jumps, with a greater APP and RPP and a lower RPE and DOMS.
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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) | 0.001 | 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".