The Correlation between Butterfly Swimming Technique with Motor Ability and Motor Educability
Bibliographic record
Abstract
The purpose of this research is to find out the correlation between butterfly swimming technique with motor ability and motor educability. The research uses a descriptive method. Data was analysed and the result shows that (1) t = 2.9084 and t table = 2.31. Where t count > t table, Ho: rejected, there was a positive and significant correlation between motor ability and butterfly swimming technique. (2) t = 2.9574 and t table value = 2.31. Where t count > t table Ho: rejected, there was a positive and significant correlation between motor educability and butterfly swimming technique. (3). Test the significant and obtained f f table count and the real level of 0.05 with df = (k), (n - k - 1) (2/7) gained in value by 4.74. The criteria for the hypothesis is : Accept the hypothesis ( Ho ) if F count + F table with df = ( k), ( n - k - 1 ) at a significance level of = 0.05, in the case of another hypothesis ( Ho ) is rejected. From the data processing obtained 5.1531 + 4.74 so it can be concluded that the hypothesis (Ho) is rejected, there was a positive and significant correlation between motor ability and motor educability on butterfly swimming technique. Based on the results, the research is expected would contribute to sport coach competency. Hence, they can strengthen the athlete's potential without ignoring a basic movement skill. It can be concluded that movement skill and motor educability has an important role to study a difficult fundamental skill movement, for instance the ability of butterfly swimming technique.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".