Hematological, Serum Biochemistry Profile and Nutrient Retention of Finisher Broilers Fed Diet in Which Bio-fermented Rice Husk Meal Substituted Brewer’s Dried Grain
Bibliographic record
Abstract
Heaps of rice husk waste is currently increasing around rice mills in Nigeria. Production cost for broilers has risen tremendously due to high cost of feed ingredients, one of which is Brewer’s Dried Grain (BDG). Rice husk has been identified as a potential feed stuff and can replace BDG in broiler production. The research determined the effect of substituting BDG with bio-fermented rice husk meal on the blood profiles and nutrient retention of the broilers. Four treatment diets were formulated. T1 (control) contained 15% BDG while in T2, T3 and T4 bio-fermented rice husk meal replaced BDG at 33.33%, 66.67% and 100% to be 5%, 10% and 15% respectively. The diets were assigned to 120 broilers of 28 days old in a complete randomized design of 10 broilers replicated 3 times. The broilers were reared for 28 days after which blood samples were collected. Nutrient retention of the diets was also analyzed. There was a significant (P < 0.05) reduction in the WBC, MCH, MCHC and serum cholesterol level of broilers on the test ingredient. Nutrient retention showed a significant (P < 0.05) increase in ash while the control and T2 were similar (P > 0.05) in crude protein. The research discovered that though there were significant results in most the hematological and biochemical parameters, but the trend of the results was not suggestive that the difference was due to diet. It was concluded that bio-fermented rice husk meal may not have a negative effect on the blood profile of finisher broilers.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".