A Modelling of the Effect of Biomotor Capabilities on the Special Ablitiy Test Course
Bibliographic record
Abstract
The aim of this study is to evaluate some bio motor capabilities which are thought to have an effect on SpecialAbility Test Course (SATC) test scores applied as an entrance exam in the School of Physical Education and Sports(SPES). 70 participants who were successful in the SATC (51 male (181 ± 5.5 cm, 73.9 ± 9.8 kg) and 19 female (165± 5.6 cm, 54.7 ± 6.49 kg) were included in the study. The bio motor capability performances of the participants, suchas speed (10 m, 20 m, 30 m, 100 m sprint), agility (T-test, 505 agility test), and anaerobic power (Sargent verticaljump test) were measured two weeks after the SATC tests. The performances of the participants in the 2018 SATC ofthe Tekirdag Namik Kemal University SPES were used as the scores of the SATC. The mean of a difference test, acorrelation analysis, and a multiple linear regression analysis were used as the statistical method (p<0,05). Whilethere was a significant positive correlation between the SATC agility (r = 799 **) and speed (r = 895 **) scores,there was a negative correlation between SATC scores and anaerobic power output (r = -719 **). Statistical analysiswas performed by taking the gender factor into account in the linear regression estimation due to the significantdifference (p <0.05) between the SATC scores according to the gender variable. The bio motor capabilities of themale participants, which contributed the most to the SATC scores, were determined as 505 agility (Beta = 424) and30 m (Beta = 379) speed (SATC Male=-9.992 + (10.478 x 505 test) + (6.742 x 30 m). However, the bio motorcapabilities of the female participants did not contribute to the SATC scores.
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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 | 0.001 | 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) | 0.002 | 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 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".