The Relationship Between Anaerobic Performance and Lower Extremity Volume and Mass in Female Athletes in Individual Sports and Team Sports
Bibliographic record
Abstract
This study aims to analyze the relationship between anaerobic performance and leg and foot volume and mass in female athletes in individual sports and team sports. 90 female athletes who have been active in sports for at least three years voluntarily participated in the study. The average age of participants is 21. Frustum and Hanavan methods were used to calculate leg volume and mass, respectively. 20-meter sprint test and vertical jump test were carried out in order to determine anaerobic performance. SPSS 22.0 package program for Windows was used for descriptive statistics and correlation analysis of the obtained data. In this study it was demonstrated that leg volume and mass of female athletes varied in each sport and that right and left leg volume and mass of these athletes were fairly close. Leg volume and mass were higher in team sports athletes than individual sports athletes. In addition, vertical jump performance was better in individual sports athletes. Therewithal it was found very highly negative significant correlation between vertical jump and 20-meter speed values in individual and team sports. As a result it was found that leg volume and mass were higher in team sports athletes than individual sports athletes. Besides when the relationship between leg volume and mass and anaerobic performance is analyzed, a very high positive significance was found between 20 meter speed and right leg mass in boxers. In this way the development of both leg volumes and mass is therefore important for a better anaerobic performance for athletes.
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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 | 0.001 | 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) | 0.003 | 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".