Physiological mechanisms of muscle strength and power are dependent on the years post obtaining peak height velocity in elite juniors rowers: A cross-sectional study
Bibliographic record
Abstract
BACKGROUND: It is not yet known whether the years after peak height velocity (PHV) are associated with the physiological mechanisms of muscle strength and power in Juniors rowers. OBJECTIVE: To identify the association between years post PHV (YPPHV) with muscle power and strength in Juniors rowers. METHODS: We tested 235 Brazilian rowing athletes (male: 171, female: 64, Juniors category). We measured: power (indoor rowing over 100-m, 500-m, 2,000-m and 6,000-m) and muscle strength (one repetition maximum (1RM) test in squat, deadlift, bench press and bent row on the bench). Biological maturation was index by age of PHV. The sample was divided into groups considering YPPHV recent (2.5 to 3.9), median (2.51 to 4.9) and veteran (>4.9). We use a Baysian approach to data handling. RESULTS: When compared to their peers in the recent and median post PHV groups, the male veteran group were superior in muscle power (Absolute: 100-m (BF10: 2893.85), 500-m (BF10: 553.77) and 6,000-m (BF10: 22.31). Relative: (100-m (BF10: 49.9)) and strength (BF10≥10.0 in squat, bench press and deadlift), and in the female the veteran group were superior in test time (500-m, BF10: 88.4). CONCLUSION: In elite Juniors rowers the increasing YPPHV are associated with muscle power performance in both sexes and muscle strength performance in males.
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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 | 0.001 | 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".