Bibliographic record
Abstract
BACKGROUND: There is increasing interest in the fecal microbiota, but study in calves has been limited. HYPOTHESIS/OBJECTIVES: To evaluate the fecal microbiota of beef calves and cows on different farms, and to preliminarily explore the impact of antimicrobial exposure. ANIMALS: A total of 172 animals, 156 (91%) calves and 16 (9.3%) cows, were enrolled from 5 cow-calf farms. METHODS: The fecal bacterial microbiota was assessed through sequencing of 16S rRNA gene (V4 region) amplicons. RESULTS: There were significant differences in the relative abundances of numerous phyla between calves on different farms. Farms could be separated into 2 groups: 1 (farms B and C) dominated by Firmicutes and 1 (farms A, D, and E) with predominance of Proteobacteria and Actinobacteria. Richness (median 2,974 versus 1,477, P = .008), diversity (51.4 versus 29.1, P = .0029), and evenness (0.73 versus 0.68, P = .006) were higher in cows. Over-represented operational taxonomic units (OTUs) in cows tended to be from the classes Bacilli and Bacteroidia, whereas Clostridia and Actinobacteria were most prominently over-represented in calves. There were differences in community membership (P = .028) and structure (P = .029) in calves that had a history of antimicrobial exposure compared those that did not. Eight (89%) over-represented OTUs in the untreated group were Firmicutes (7 from the order Clostridiales), compared to only 3 (38%) (2 Clostridiales) in the untreated group. CONCLUSIONS AND CLINICAL IMPORTANCE: Interfarm variation should be investigated to determine the causes and potential implications for health and production. Antimicrobial exposure may have an impact on the fecal microbiota at individual and farm levels.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".