Examination of Stroke Mechanics and Athletic Performance Components in Swimmers According to Age Categories
Bibliographic record
Abstract
At the beginning of the components that affect the performance of the swimmers’ training and race planning, the stroke count (SC), stoke lenght (SL) and stroke rates (SR) of the swimmers are important. For this reason, it is important for both athletes and coaches to know how to develop and change these characteristics with age. The aim of this study is to investigate the change in athletic performance and body structure of 9–15 age group. A total of 50 students were participated in the study. Subjects were divided into two groups as below 12 years of age (< 12 years, nmale = 14, nfemale = 14) and 12 years of age and older (≥ 12 years, nmale = 11, nfemale = 11). The athletic performance variables are grouped in such a way as jump (vertical and horizontal) and swim data (50 m free swimming time, number of SR, SC, SL). Descriptive statistics were used for the groups and Mann Whitney U test was used for comparison between groups (p < 0.05). A significant difference was found between all groups except SR, and in the jump performance (horizontal & vertical) with age. There was no significant difference in stroke rate and stroke time in kinematic parameters; as a result of the significant difference found in the SC and SL, 50 m freestyle swimming times decreased with age. As a result; it is thought that following this changing process for each age group of coaches will contribute significantly to the swimming performances of the 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) | 0.002 | 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".