The impacts of feeding milk with antibiotics on the fecal microbiome and antibiotic resistance genes in dairy calves
Bibliographic record
Abstract
The effects of ingestion of antibiotics on the microbiome of the young calf are not well understood. The objective of this study was to evaluate the effect of feeding milk containing pirlimycin on the prevalence of antibiotic resistance genes in the fecal microbiome of dairy calves using a metagenomic approach. In this study calves were assigned to either pasteurized whole milk (control; n = 5) or pasteurized whole milk containing 0.2 mg L−1 of pirlimycin (treatment; n = 5). Fecal samples were collected on days 1, 42, and 84. Functional analysis of DNA via metagenomic rapid annotations using subsystems technology revealed that pirlimycin had no effects on abundance of sequences coding for different cell functions except in the “phage, prophage, and transposable elements” category. Evaluation of the major antibiotic resistance types in samples via annotation against the Comprehensive Antibiotic Resistance Database analysis showed no difference between the two groups. The results of this study will help assess the risk of use of antibiotics in animal agriculture and increase our understanding of how antibiotics present in waste milk affects both calves and their manure, and will lay the groundwork for future research on manure treatment or other strategies to minimize any negative impacts.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".