Effects of Amino Acid Composition in a <I>Bacillus amyloliquefaciens</I>-fermented Mixture of Bovine Blood and Coconut Pulp on Growth Performance, Blood Cholesterol of Broilers
Bibliographic record
Abstract
Background and Background: Bovine blood is a livestock by-product that can be used as a protein source for livestock, particularly when incorporated in broiler rations. This study was conducted to investigate the effects of amino acids in Bacillus amyloliquefaciens-fermented mixtures of bovine blood and coconut pulp (blood meal) on growth performance, blood cholesterol and erythrocyte content in broilers. Methodology: A total of 100 six day-old CP 707 (Strain Cobb) broilers were divided into 5 experimental groups with 4 replicates of 5 broilers. The experimental groups received rations supplemented with 0, 5, 10, 15 and 20% blood meal that substituted for fish meal or soybean meal. The experimental period was 5 weeks. Treatment effects on broiler performance parameters such as final body weight, final weight gain, average daily gain, feed intake, average abdominal fat and Feed Conversion Ratio (FCR), total serum cholesterol and erythrocyte count, as well as the return on investment were determined. Results: The group fed rations supplemented with 10% blood meal showed final body weight, final weight gain and FCR of 1,172, 1,035 and 1.75 g, respectively, which was significantly higher (p<0.05) than that for the other treatments. Rations with 10% blood meal also had the best return on investment ($0.92) relative to the other groups. Conclusion: A mixture of 10% bovine blood and coconut pulp fermented with Bacillus amyloliquefaciens can be used in broiler feed without adversely affecting broiler performance and can replace 47% of total fishmeal and 53% of soybean meal in broiler rations.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".