Penambahan Vitera Plus Dalam Air Minum Terhadap Pertambahan Berat Badan Ayam Pedaging
Bibliographic record
Abstract
The development of the broiler business is very volatile due to relatively unstable meat prices and high feed prices. One product to increase the level of broiler feed efficiency is to provide viterna plus. The purpose of this study was to determine the level of viterna plus in drinking water on the growth of broiler chicken weight. The material in this study was 100 DOC strains CP 707. Treatment Ro = viterna plus 0 cc/liter, R1 = plus 1 cc/liter, R2 = viterna plus 2 cc/liter, R3 = viterna plus 3 cc/liter. Weight gain in the first week there was a marked difference (P<0.05) where R0 was different from R2, but R1 and R3 were not significantly different from R0 or R2. Weight gain at weeks 2 to 6 is no different. In general, giving viterna plus has a very small effect, this is seen with low to high levels, namely in R1, R2 and R3 are no different. The conclusion is that the addition of viterna plus given through broiler chicken drinking water at a usage rate of 2 cc/head can improve weight gain. It is recommended to use 2 cc / head in order to increase daily weight gain, adding more than 2 cc / head does not increase weight gain.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.057 | 0.017 |
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".