Characterization of the microbiota and resistome of bulk tank milk samples from Prince Edward Island dairy farms
Bibliographic record
Abstract
Bulk tank milk (BTM) is regularly used for surveillance on dairy farms for disease conditions such as mastitis and Johne's disease. In this study, we used 16S rRNA sequencing and bait-capture enrichment to characterize the microbiota and resistome of BTM, and investigate potential differences between the cream or pellet fractions. A total of 12 BTM samples were taken from 12 Prince Edward Island dairy farms, in Atlantic Canada, in duplicates. The DNA was analyzed by high-throughput sequencing of the 16S rRNA gene and a suite of antimicrobial resistance (AMR) genes. Target-capture enrichment of AMR genes was conducted before shotgun sequencing. The bioinformatics pipelines QIIME 2 and AMR++ were used for microbiota and resistome analysis, respectively. Differences between microbiotae were evaluated qualitatively with nonmetric multidimensional scaling and quantitatively with permutational ANOVA of UniFrac distances. A total of 47 phyla were present across the BTM samples. Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria were the most abundant phyla. At the genus level, Corynebacterium, Acinetobacter, Lactobacillus, and Turicibacter were the most abundant. There was no significant difference in the Faith's phylogenetic diversity between the cream and pellet fraction. Faith's phylogenetic diversity differed marginally by stall type. There were 10,217 hits across 80 unique AMR genes, with tetracycline resistance being the most common class.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".