ANALISIS KETERAMPILAN DASAR SEPAK BOLA PEMAIN KLUB BIMA SAKTI
Bibliographic record
Abstract
Penelitian ini dilatar belakangi oleh masih kurangnya keterampilan dasar sepak bola pemain Klub Bima Sakti Tahun 2018. Penelitian ini dilakukan dengan tujuan untuk mengetahui keterampilan dasar bermain sepak bola. Rancangan penelitian ini termasuk penelitian deskriptif dengan pendekatan kualitatif. Metode yang digunakan ialah survey dengan teknik pengumpulan data menggunakan tes dan pengukuran. Populasi penelitian ini adalah semua pemain Klub Bima Sakti dengan jumlah subyek penelitian 20 pemain atau menggunakan teknik studi populasi. Teknik analisis data yang digunakan adalah teknik analisis data statistik deskriptif yaitu teknik mengelompokan data hasil t score kedalam lima kategori norma tes sepak bola kemudian data yang telah dikelompokan dalam kategori kriteria keterampilan dasar sepak bola dihitung persentase keterampilan dasar sepak bola dengan menggunakan rumus persentase . Dan dapat disumpulkan bahwa kemampuan dasar sepak bola pemain Klub Bima Sakti tergolong dalam kategori cukup dengan persentase 50%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.008 |
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".