KONTRIBUSI FLEKSIBELITAS TERHADAP KECEPATAN RENANG GAYA DOLPHIN (TANPA GERAKAN TANGAN ) PADA \nMAHASISWA PENJAS UNILA ANGKATAN 2012 \nTAHUN AJARAN 2013
Bibliographic record
Abstract
Penelitian ini bertujuan untuk mengetahui Kontribusi Fleksibelitas terhadap kecepatan renang gaya dolphin (tanpa gerakan tangan ) pada Mahasiswa penjas unila angkatan 2012 Tahun ajaran 2013 \nMetode penelitian yang digunakan adalah penelitian kwantitatif, serta menggunakan pada Mahasiswa penjas unila angkatan 2012 Tahun ajaran 2014. Populasinya merupakan populasi sample yang berjumlah 60 mahasiswa. \nDari hasil penelitian di dapat bahwa ada Kontribusi Fleksibelitas terhadap kecepatan renang gaya dolphin (tanpa gerakan tangan ) sebesar 0,73 (53,29 %). Hal ini berarti adanya hubungan yang positif antara Fleksibelitas terhadap kecepatan renang gaya dolphin (tanpa gerakan tangan ) cabang olahraga renang. Oleh karena itu seorang pelatih atau guru agar dapat meningkatkan kemampuan Fleksibelitas dengan memberikan latihan yang sifatnya melatih Fleksibelitas tanpa melupakan komponen yang lainnya. \nKesimpulan dari penelitian ini adalah Fleksibelitas memberikan hubungan yang positif terhadap kecepatan renang gaya dolphin (tanpa gerakan tangan ) pada Mahasiswa penjas unila angkatan 2012 Tahun ajaran 2013 \nKata kunci: Renang, Gaya Dolphin Fleksibelitas dan Kecepatan
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.011 | 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".