Influence of Muscle Architecture on Maximal Rebounding in Young Boys
Bibliographic record
Abstract
ABSTRACT: Radnor, JM, Oliver, JL, Waugh, CM, Myer, GD, and Lloyd, RS. Influence of muscle architecture on maximal rebounding in young boys. J Strength Cond Res 35(12): 3378-3385, 2021-The aims of the current study were to (a) investigate differences in maximal rebound jump kinetics in boys at different stages of maturity and (b) determine the relationship of muscle architecture characteristics to maximal rebound jump kinetics. One hundred twenty-seven male, secondary school children were categorized into maturity groups (pre-, circa-, and post-peak height velocity) based on their maturity offset value. Muscle architecture of the gastrocnemius medialis (GM) and vastus lateralis (VL) was evaluated at rest using B-mode ultrasonography. Subjects then performed maximal rebound jumps quantified on a force platform. There were moderate to large differences across all maturity groups for peak ground reaction force, impulse measures, and average power variables (d = 0.73-2.67; p < 0.05). Gastrocnemius medialis and VL muscle thickness explained between 38.5 and 55.8% of the variance in peak force, impulse, and power variables; however, muscle architecture was less important determinant of contact time, jump height, reactive strength index, rate of force development, eccentric velocity, concentric velocity, and allometrically scaled measures (3.3-17.2%). The current results indicate that most kinetics used during maximal vertical rebounding are greater in more mature boys. Furthermore, maturational increases in GM muscle architecture seem important for maximal vertical jumping and are specifically associated with increased force, power, and impulse measures. Practically, these findings may underline benefits in targeting resistance training activities that are focused to increase lower limb muscle mass to positively influence maximal rebounding kinetics in young boys.
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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".