Efektivitas Stretching PNF Terhadap Kelenturan
Bibliographic record
Abstract
Tujuan dari penelitian ini adalah untuk mengetahui tingkat efektivitas Stretching PNF pada kelenturan untuk meningkatkan fleksibilitas. Metode penelitian dengan menggunakan eksperimen One Group Pretest- Postest design. Sampel yang digunakan adalah mahasiswa/i Universitas Tanjungpura Prodi Pendidikan Kepelatihan Olahraga berjumlah 10 orang. Penelitian ini dilakukan dengan menggunakan treatmeant 3 gerakan stretching PNF, dengan gerakan Hold Relax Hamstring, Quadriceps, Chest PNF. Kemudian diukur dengan sit and reach sebelum dan sesudah perlakuan (treatmeant). Teknik analisis data menggunakan deskriptif dan uji t. Hasil penelitian ini mengungkapkan terdapat perbedaan antara sebelum dan sesudah perlakuan sebesar 1,8 cm. Maka dengan dapat simpulkan bahwa dengan adanya peregangan PNF dapat meningkatkan kelenturan
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.019 | 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".