The effects of galacto-oligosaccharides on faecal parameters in healthy dogs and cats
Bibliographic record
Abstract
The aim of this study was to evaluate the effects of galacto-oligosaccharides (GOS) on faecal parameters in healthy dogs and cats. To this end, 20 dogs and 20 Domestic shorthair cats were fed a commercially available adult dog food, or cat food, respectively, with either syrup containing GOS (at 1% w galacto-oligosaccharides/w formulated feed) on top (test group) or no topping (control group) for 56 days in a cross-over design. The study consisted of 2 periods of 24 days adaptation, followed by 4 days of collection of faeces. Faecal samples were tested for moisture, nitrogen, pH, macronutrients, enzymes, and fermentation products. The faecal microbiota were analysed by 16S rDNA profiling. It appeared that GOS have different effects in dogs compared to cats. In dogs, the addition of GOS resulted in increased carbohydrate fermentation (increase of acetic and butyric acid), whereas in cats GOS resulted in increased amino acid fermentation (increase of isovaleric acid). The α-diversity of the canine faecal microbiota was reduced by dietary GOS (Inverse Simpson Index, p = 0.063; Shannon index, p = 0.035) whereas the α-diversity of cat faecal microbiota was unaffected (Inverse Simpson Index, p = 0.539; Shannon index, p = 0.872). Lachnospiraceae spp. and Bifidobacterium spp. positively responded to GOS in both cats and dogs. Lactobacillus spp. and Enterobacteriaceae spp. positively responded to GOS in dogs. In both dogs and cats, GOS may therefore improve stool microbiota and result in the production of specific metabolites that are beneficial to gut health.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".