Effects of fermented dried brewer's grain on performance, carcass characteristics, morphology of ileum and blood parameters in broiler chickens
Bibliographic record
Abstract
This experiment investigated the effects of fermentation on the chemical composition of dried brewer's grain (DBG) and its inclusion in broiler diets on performance, carcass traits, intestinal histology, and blood proteins. The DBG was fermented for 21 days using Bacillus subtilis and Aspergillus oryzae. A total of 360 male broilers (Ross 308) were fed with 10 % or 20 % DBG or fermented DBG (FDBG) for 42 days. Results revealed that fermentation could reduce crude fiber, pH, nitrogen-free extract, neutral detergent fiber, acid detergent fiber, and total bacterial count whereas it increased Lactobacillus count as well as fat, ash, calcium, and phosphorus contents (P<0.01). Broilers fed 10 % FDBG exhibited a similar performance to the control group. Serum levels of total protein and globulin were significantly higher in broilers that received 10 % FDBG compared to the control (P<0.01). However, feeding broilers with 20 % DBG or 20 % FDBG reduced weight gain and worsened the feed conversion ratio (P < 0.001). Moreover, broilers that received 20 % DBG had a shorter ileal villus height compared to the control group (P=0.034). These findings suggest that microbial fermentation could improve the nutritional quality of DBG, and the fermented product (FDBG) could be included in broiler diets up to 10 % without impacting performance.
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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 | 0.001 | 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".