94 Alteration of fecal bacterial composition in pre-weaned veal calves by supplementation of Saccharomyces cerevisiae boulardii in milk replacer.
Bibliographic record
Abstract
This study aimed to identify the effect of supplementing Saccharomyces cerevisiae boulardii CNCM I-1079 (SB) in milk replacer on the fecal bacterial composition of veal calves. Veal calves (6 d ± 3 d of age, n = 84) were randomly assigned to two treatments: CON (no SB; n = 42 calves) and SB (n = 42 calves). From the first day of arrival, SB was added into milk replacer (supplying 10 X 10Firmicutes (65.3%), Actinobacteria (11.7%) and Proteobacteria (11.0%). Among them, 5 phyla were detected (defined as the relative abundance being above 0.1% and present in at least half of the samples in each group). A total of 63 bacterial genera were detected, predominately Faecalibacterium (15.0%), Collinsella (10.5%), and Escherichia-Shigella (9.3%). In SB calves compared to controls, the relative abundance of Escherichia-Shigella was lower (P < 0.05), and those of Dorea and Streptococcus tended to be lower (P < 0.1). Sampling time affected the relative abundance of Faecalibacterum, Escherichia-Shigella, Bacteroides, and Dorea (P < 0.05), and tended to affect that of Collinsella and Streptococcus (P < 0.1). Sampling time and supplementation of SB interactively affected the relative abundance of Escherichia-Shigella (P < 0.05) and tended to affect that of Faecalibacterium, Streptococcus, Lachnospiraceae UCG-008, and Dorea (P < 0.1). These results showed that SB could alter the composition of the fecal bacteria in a time-dependent manner, suggesting that SB could be capable of manipulating gut bacteria in pre-weaned veal calves. Key Words:
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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 | 0.000 |
| 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".