PENGARUH PENGGUNAAN SILASE LIMBAH SAWI PUTIH (BRASSICA PIKENENSIA L.) TERHADAP KONSUMSI DAN KECERNAAN ENERGI DAN PROTEIN PADA TERNAK BABI GROWER
Bibliographic record
Abstract
Tujuan dari penelitian ini adalah untuk mengetahui pengaruh penggunaan silase limbah sawi putih (Brassica pekinensia L) dalam ransum terhadap konsumsi dan kecernaan energi dan protein pada babi grower. Ternak yang digunakan adalah babi peranakan landrace fase grower sebanyak 12 ekor yang berumur 3-4 bulan dengan bobot badan awal berkisar 29-52 kg dan rataan 36 kg (KV = 17,72%). Penelitian ini menggunakan metode percobaan dengan Rancangan Acak Kelompok (RAK) yang terdiri dari empat perlakuan dan tiga ulangan sehingga terdapat 12 unit percobaan. Perlakuan yang digunakan adalah R0: 100% ransum basal, R1: 90% ransum basal + 10% silase limbah sawi putih, R2: 85% ransum basal + 15% silase limbah sawi putih dan R3: 80% ransum basal + 20% silase limbah sawi putih. Variabel yang diteliti adalah konsumsi ransum, konsumsi energi, konsumsi protein, kecernaan energi dan kecernaan protein. Hasil penelitian menunjukkan bahwa perlakuan berpengaruh tidak nyata (P>0.05) terhadap semua variable. Berdasarkan hasil penelitian ini, dapat disimpulkan bahwa penggunaan silase limbah sawi putih (Brassica pekinensia L) dalam ransum pada level 10%, 15% dan 20% memberikan pengaruh yang sama terhadap konsumsi dan kecernaan energi dan protein. Disarankan limbah sawi putih dapat diolah menjadi silase dan dapat digunakan 20% mengganti ransum babi grower. Kata kunci: babi grower, energi, protein, silase limbah sawi putih.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".