Investigation of Physical and Motor Characteristics Between University Students Who Participate and Students Who Don’t Participate in Sport Activities
Bibliographic record
Abstract
The purpose of this study is to investigate the differences between physical and some motoric characteristics of university students who participate in regular sports training and those who do not. A total of 20 volunteer students, 10 students with an average age of 20.80 ± 1.69 and 10 students with an average age of 19.90 ± 1.45 without any exercise habits were included in the study. Body circumference measurements, vertical-horizontal jump and strength, flexibility, body mass index, body fat ratio, body circumference measurements, vertical-horizontal jump and claw-back-leg strengths, flexibility, peak and average power values of the participant groups were determined and compared. SPSS 20.0 package program was used to analyze the data. Athlete Students' body density, shoulder circumference, chest circumference, biceps (extension-flexion) circumference, average-peak strength, long-vertical jump, hand claw-back-leg strength were determined to be higher than non-athlete students (p<0.05). Besides, students doing sports; body weight, body mass index, body fat ratio and waist circumference measurement averages were found to be lower than non-athletes (p<0.05). As a result; it has been observed that the participation of young students in regular sports educations can be effective in preventing the risk of obesity and increases their motoric characteristics.
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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 | 0.001 |
| 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".