PENGARUH TINGKAT PEMBERIAN KONSENTRAT TERHADAP KUALITAS SUSU SAPI PERAH FH PERIODE LAKTASI KE-3
Bibliographic record
Abstract
Abstrak Penelitian ini bertujuan untuk mengetahui tingkat pemberian konsentrat terbaik untuk mendapatkan produksi susu dengan kualitas yang baik dan pakan efisiens pada sapi perah di wilayah kerja KUD Karya Nugraha Kelurahan Cipari Kecamatan Cigugur Kabupaten Kuningan. Penelitian dilaksanakan selama 2 (dua) minggu yaitu 22 September - 4 Oktober 2008 di KUD Karya Nugraha Kelurahan Cipari Kecamatan Cigugur Kabupaten Kuningan dengan menggunakan 24 ekor sapi laktasi ke 3 yang diberi pakan rumput gajah dengan bahan kering BK 22,2 % dan kadar protein 8,69 % dan konsentrat dengan BK 87,3% dan kadar protein 18% dengan kombinasi pemberian sesuai perlakuan dan diulang 4 kali. Kombinasi pemberian pakan yaitu : A= Konsentrat 25% dari BK pakan; B = Konsentrat 30% dari BK; C=Konsentrat 35% dari BK; D = Konsentrat 40% dari BK; E=Konsentrat 45% dari BK; F=Konsentrat 50% dari BK. Hasil dari penelitian ini yaitu bahwa pemberian pakan konsentrat dengan kadar 40 – 50% dari BK kebutuhan pakan menghsilkan susu sapi dengan kualitas yang baik sesuai dengan ketentuan Milk Codex yaitu Kadar lemak 3,68 – 3,70 % , SNF 8% dengan pemberian pakan yang paling efisien.Kata Kunci : Konsentrat, Susu Sapi Perah FH dan laktasi ke-3
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.048 | 0.011 |
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".