Relationship of Biological Maturation with Muscle Power in Young Female Athletes
Bibliographic record
Abstract
There is still no consensus on how biological maturation (BM) affects the muscle power of upper and lower limbs in young people. The objective was to verify associations between BM and muscle power, as well as to compare the muscle power of upper (ULP) and lower limbs (LLP) among young athletes in different stages of BM. The sample consisted of 79 female athletes (10.9 ± 1.11 years old). Regarding BM, the sample was divided into three groups: delayed BM, synchronized BM, and accelerated BM. BM was identified by subtracting chronological age from bone age (BA). BA was measured by a mathematical model based on anthropometry. The muscular power of the upper limbs was analyzed by the medicine ball launch test, and that of the lower limbs was analyzed by the countermovement jump on a force platform. BM and BA correlated with ULP (BA: r =0.74; BM: r =0.65) and LLP (BA: r = 0.50; BM: r =0.41). In the comparisons of the tests of ULP and LLP, the groups with synchronized and accelerated BM were superior to the group with delayed BM. The advance of BM is associated with the ULP and LLP, as well as the advance of the BM affects muscle power in young female athletes. This occurs due to the increase in body mass resulting from the advancement of BM, which may favor the predominance of lean body mass, assisting in the production of muscle strength.
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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 | 0.002 |
| 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) | 0.001 | 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".