PERFORMAN AYAM PETELUR UMUR 40 HARI -75 HARIYANG DIBERI EKSTRAK TEMULAWAK(Curcuma xanthorrizaRoxb) DENGANKONSENTRASI \nYANG BERBEDA
Bibliographic record
Abstract
Penelitian ini bertujuan untuk melihat performan (konsumsi ransum, pertambahan bobot badan, konversi ransum) ayam petelur yang diberikan ekstrak temulawak dengan konsentrasi yang berbeda. Penelitian ini telah dilakukan di Kandang Percobaan Fakultas Pertanian dan Peternakan UIN Suska Riau. Penelitian ini berlangsung selama 35 hari, dimulai bulan September – Oktober 2013. Rancangan yang digunakan adalah rancangan acak lengkap (RAL) yang terdiri dari 4 perlakuan dan 7 ulangan. Perlakuan terdiri dari atas T0 ( 0 ml ekstrak temulawak), T1 (1 ml ekstrak temulawak), T2 ( 2 ml ekstrak temulawak), dan T3 (3 ml ekstrak temulawak). Ekstrak temulawak diberikan setiap hari dengan cara dicekok selama penelitian. Hasil penelitian menunjukkan bahwa pemberian ekstrak temulawak tidak berbeda nyata (P>0.05) terhadap konsumsi ransum rataannya yaitu 312.94 ± 11.88 g/ekor/minggu, rataan pertambahan bobot badan yaitu 254.10 ± 20.70 g/ekor/minggu dan konversi ransum yaitu 1.33 ± 0.24 . Pemberian ekstrak temulawak sampai dengan 3 ml tidak dapat meningkatkan konsumsi ransum, pertambahan bobot badan dan memperbaiki konversi ransum ayam petelur. Perlu dilakukan penelitian yang sama terhadap performan ayam petelur dengan penambahan level ekstrak temulawak dengan konstentrasi yang berbeda. \nKata kunci : Performan ayam petelur, ekstrak temulawak, konsentrasi yang berbeda.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.009 |
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".