The Effect of Two Semester Wrestling Training on University Students’ Body Composition and Some Motoric Characteristics
Bibliographic record
Abstract
The aim of this study was to examine the changes in the body composition and motor characteristics of the students attending the wrestling lesson in one academic year (8 months). The study included 19 male wrestler students with an average age of 21.20±1.61 years. Body weights, body circumference measurements, regional muscle strengths, anaerobic strength skinfold thicknesses and body fat percentages were measured twice before the start of the wrestling training at the beginning of the academic year. SPSS 20.0 package program was used to analyze the data obtained at the beginning and end of the season. In the evaluation of pretest and posttest measurements, wrestler students’ body weights, BMI, shoulder circumference, chest circumference, bicep circumference (ext), bicep circumference (flx), hip circumference, upper leg circumference, vertical jump distance, long jump distances, Anaerobic power capacities There was a statistically significant difference between the back force, leg strength, biceps, triceps and abdominal skin folds (p<0.05). As a result, it was determined that wrestling training taken by university students in a training process had a positive effect on muscle strength and anaerobic power levels and a positive effect on the development of body circumference measurements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 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".