Effects of Galacto-Oligosaccharides and a Bifidobacteria lactis-Based Probiotic Strain on the Growth Performance and Fecal Microflora of Broiler Chickens
Bibliographic record
Abstract
A galacto-oligosaccharide (GOS) prebiotic was prepared by reacting a high concentration of lactose (40% wt/vol) with a beta-galactosidase enzyme for 24 h at 37 degrees C. The enzyme was produced from recombinant Pichia pastoris X-33 cells. The study aimed at evaluating the effects of the prebiotic, a Bifidobacterium lactis-based probiotic, and the combination of these dietary additives on BW, feed intake, feed conversion ratio, and fecal counts of total anaerobic bacteria, lactobacilli, and bifidobacteria in broiler chickens. No significant differences in BW, feed intake and feed conversion ratio were found among the various groups. The study showed that GOS selectively stimulated the fecal microflora of broiler chickens. Total anaerobic bacteria and lactobacilli were increased by 3.4- and 3.56-fold, respectively, in chickens fed the diet containing GOS (3 kg per 25 kg) and B. lactis for 40 d compared with those fed the control diet. The bifidobacteria population in chickens fed the diet containing GOS (3 kg per 25 kg) and B. lactis significantly increased 21-fold in comparison to the control-fed birds. In particular, increasing the dietary concentration of GOS was accompanied by significant increases (P < 0.05) in bifidobacteria counts. The detectable population of bifidobacteria was also greater (P < 0.05) in chickens fed the diet containing GOS and bifidobacteria when compared with chickens fed a bifidobacteria-containing ration only. These results suggest that using GOS in combination with a B. lactis-based probiotic favored intestinal growth of bifidobacteria in broiler chickens.
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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".