POTENSI BIOMASSA TERUBUK (SACCHARUM EDULE HASSKARL) SEBAGAI PAKAN UNTUK PERTAMBAHAN BOBOT BADAN SAPI
Bibliographic record
Abstract
Selain dikonsumsi sebagai sayuran, terubuk juga mempunyai potensi sebagai pakan. Diharapkan dapat mengatasi kendala utama yang dihadapi petani dalam meningkatkan produktivitas sapi. Ini dapat menjadi salah satu alternatif dalam meningkatkan kesejahteraan petani dengan sistem usahatani terpadu. Penelitian ini bertujuan mengidentifikasi potensi biomassa tanaman terubuk, menganalisis pengaruh pakan dari limbah terubuk terhadap pertambahan bobot badan sapi. Penelitian dilaksanakan di Kecamatan Luwuk Timur Kabupaten Banggai Sulawesi Tengah dan Laboratorium Teknologi Hasil Pertanian Universitas Tadulako Palu. Untuk mengidentifikasi potensi biomassa terubuk yaitu dengan cara menghitung biomassa basah dan kering dengan menghitung luas lahan x jarak tanam x berat segar. Sedangkan analisis laboratorium dilakukan untuk menghitung bahan kering, protein kasar, lemak kasar dan serat kasa. Pertambahan bobot badan sapi dihitung dengan cara menimbang bobot badan sapi sebelum dan setelah pemberian pakan ternak dari terubuk. Hasil penelitian menunjukkan jika ketersediaan limbah terubuk rata-rata sebanyak 11.300 kg/ha maka limbah terubuk mampu memenuhi kebutuhan ternak 4 ekor sapi selama 90 hari. Sedangkan potensi kualitas tanaman terubuk mengandung bahan kering 13%, protein kasar 3,15% dan lemak kasar 1,28% serta mengandung serat kasar 41, 27% dan BETN 42,41%. Pertambahan berat badan ternak sapi yang diberi terubuk sebanyak 7,5 kg pagi dan sore memberikan pertambahan berat badan 0,03 kg/ekor/hari.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".