269 Use of dietary carbohydrates as prebiotic in swine diets
Bibliographic record
Abstract
Using dietary antibiotics as growth promotant will be reduced; thus, dietary alternatives are being investigated. Dietary carbohydrates include oligosaccharides, starch, and fiber (non-starch polysaccharides) and these may be part of a toolkit to manage gut health in pigs. Antibiotics are hypothesized to control gut health via manipulations of intestinal microbial profiles but may also reduce intestinal inflammation. Oligosaccharides may be rapidly fermented and thereby influence intestinal microbial profiles and metabolite production. Specific exopolysaccharides from Lactobacillus reuteri may serve as scavenger molecules for pathogenic bacteria, e.g., enterotoxigenic E. coli (ETEC), to bind to instead of adhering to the gut wall, thereby avoiding diarrhea initiation by ETEC. Starch is mostly digested and absorbed as glucose; however, resistant starch is not digested but fermented. Resistant starch acts as fiber but is unique, because it 1) specifically increases digesta content of bifidobacteria that have been associated with improved gut health and 2) is completely fermented within the gut. Sources of fiber differ in their 2 key characteristics: viscosity and fermentability. Increased viscosity has been associated with increased gut content of virulence factors that are linked with diarrhea. Increased kinetics of fiber fermentation is associated with changes in microbial profiles and increased metabolite production. Recently, microbial composition was hypothesized to be less important and the focus should be on their combined output of metabolites. Raw materials and prebiotic feed additives both influence kinetics of fermentation and have prebiotic activity. Their kinetics of fermentation should be quantified so that it can be included in feed formulation. In conclusion, dietary carbohydrates via their prebiotics activity are part of the solution to remove antibiotics as growth promotant from swine diets.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".