PENGARUH PENGGUNAAN MINYAK KELAPA DALAM RANSUM TERHADAP BOBOT BADAN AKHIR, BOBOT DAN PERSENTASE KARKAS, SERTA PERSENTASE LEMAK ABDOMINAL PADA AYAM BURAS SUPER
Bibliographic record
Abstract
UTILIZATION EFFECT OF COCONUT OIL IN RATION ON BODY WEIGHT, PERCENTAGES OF CARCASS AND ABDOMINAL FAT IN SUPER NATIVE HENS. This study was conducted to evaluate utilization effect of coconut oil in ration on body weight, percentages of carcass and abdominal fat in super native hens. This study was involving hundred unsexed super native hens at ages of eight weeks with the average initial body weight of 862.24 g ± 44.13 g. The treatments were ration without coconut oil (CO) utilization (R0), ration of 99.5% basal added with 0.5% CO (R1), ration of 99% basal added with 1% CO (R2), ration of 98.5% basal added with 1.5% CO (R3), and ration of 98.0% basal added with 2.0% CO (R4). The completely randomized design was applied as design with five treatments consisted of five replications at each treatment. Each experimental unit was put four heads of super native hens. The significant treatments were tested by Duncan’s test. Variables measured were life body weight, slaughter body weight, carcass weight and carcass percentage as well as abdominal percentage. Results showed that utilization effect of coconut oil in ration had the same effects on percentages carcassand abdominal fat, but had significant effect on life body weight and carcass weight. Therefore, it was concluded that utilizatilization of coconut oil up to 2 percents in ratio increased life body weight and carcass weight. Keyword: carcass weight, coconut oil, super native chicken.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".