Roles of Fructooligosaccharides and Phytase in Broiler Chickens: Review
Bibliographic record
Abstract
Supplementing prebiotics and enzymes into poultry diets are among the most effective strategies in order to improve nutrient utilization, growth performance, intestinal development, immune system, intestinal microbiome and gut health. Fructooligosaccharides (FOS) is one of the most common prebiotics used in poultry. It has been reported that dietary FOS supplementation in broilers improved body weight gain, feed conversion and carcass yield. It could also enhance intestinal development, improve immune responses and increase short chain fatty acid fermentation of the broilers. Furthermore, Salmonella infection has been reduced by FOS supplementation into broiler diets. Phytase supplementation is one of the successful enzyme application in poultry. Phytase supplementation has increased body weight gain, Ca and P utilization and bone development in broilers. The combination of prebiotics and phytase, based on the modes of action of each component has shown potential benefit in poultry. Prebiotics is capable of increasing gut fermentation, producing short chain fatty acid and reducing gut pH. It has been hypothesized that prebiotics supplementation could create an acidic environment, which is favorable for phytase, increasing phytase activity and P utilization in the intestine. Therefore, the combination of prebiotics and enzyme could be a potential strategy to improve gut health and nutrient utilization in poultry.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".