The Effect of the Relationship among Leg Volume, Leg Mass and Flexibility on Success in University Student Elite Gymnasts
Bibliographic record
Abstract
This study aims to analyze the effect of the relationship among leg volume, leg mass and flexibility on success inuniversity student elite gymnasts. It was conducted on Haliç University, Marmara University, Çukurova Universityand University of Kırşehir Ahi Evran gymnastics teams (male and female) which took part in Turkish IntercollegiateGymnastics Championship and, later, voluntarily participated in this study. While years of age, height and weight ofmale gymnasts participating in the study were 21.20±1.57, 174.00±4.57 and 67.60±6.46 kg, respectively, the samevalues were 21.00±2.65 years of age, 165.31±4.60 cm and 54.62±4.63 kg for female gymnasts, respectively.Spearman correlation analysis was performed using SPSS 22.0 package program for Windows, the level ofsignificance was taken as 0.05. The analysis results indicate a highly positive significant correlation (r=.761, p<0.001)between leg volume and leg mass in male gymnasts, a highly positive significant correlation (r=.674, p<0.01)between leg volume and leg mass in female gymnasts, a highly positive significant correlation (r=.795, p<0.001)between leg volume and leg mass in male and female gymnasts, a low positive significant correlation (r=.361,p<0.05) between leg volume and success in male and female gymnasts, and a moderate positive significantcorrelation (r=.463, p<0.05) between leg mass and success in male and female gymnasts. As a result, gymnastics as asport requires a combination of speed, strength, endurance, agility, and flexibility. Speed, strength, agility andflexibility are important parameters for training and performance. In addition, an optimal amount of leg volume(13000 ml, 14000 ml) and leg mass (13-14 kg) contribute to success in elite gymnasts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".