Pengaruh Periode Laktasi Terhadap Kualitas Susu Sapi Perah FH di Wilayah Kerja Karya Nugraha Kabupaten Kuningan
Bibliographic record
Abstract
ABSTRAKPenelitian ini bertujuan untuk mendapatkan data kualitas susu sapi perah FH setiap periode laktasi dan kualitas susu terbaik pada periode laktasi di wilayah kerja KUD Karya Nugraha Kelurahan Cipari Kecamatan Cigugur Kabupaten Kuningan. Penelitian ini dilaksanakan selama 2 (dua) minggu, yaitu 4-18 Oktober 2008 di KUD Karya Nugraha Kelurahan Cipari Kecamatan Cigugur Kabupaten Kuningan dengan menggunakan 28 sapi perah laktasi, yaitu masing-masing 4 ekor sapi laktasi ke 1,2,3,4,5,6 dan 7 yang di beri pakan sama didasarkan atas kebutuhan berat kering (BK) pakan yang terdiri atas 60% rumput Pennisetum purpurium dan 40% konsentrat. Penelitian dilakukan menggunakan metode eksperimen dengan rancang acak lengkap dengan 7 perlakuan dan 4 kali ulangan. Perlakuan yang digunakan adalah periode laktasi, yaitu : I = sapi laktasi ke 1, II = sapi laktasi ke 2, III = sapi laktasi ke 3, IV = sapi laktasi ke 4, V = sapi laktasi ke 5, VI = sapi laktasi ke 6, VII = sapi laktasi ke 7. Kesimpulan dari penelitian ini adalah bahwa kualitas susu sapi perah di Wilayah Karya Nugraha Kabupaten Kuningan telah memenuhi syarat yang ditetapkan oleh Milk Codex sebagai susu yang berkualitas baik. Periode laktasi ke 4 menghasilkan susu terbaik, yaitu kadar lemak susu 3,71 %, SNF 8,02 %, Protein 3,01% dan laktosa 4,20 %.Kata Kunci : Sapi Perah FH, Laktasi, Kualitas Susu
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".