Differences in milk microbiota between healthy cows and those with recurring Klebsiella mastitis
Bibliographic record
Abstract
Abstract Klebsiella spp. infections are an important cause of severe clinical mastitis and recurrent infections, resulting in a poor response to antimicrobial agents and causing substantial economic impacts on the dairy industry. Therefore, investigating underlying causes of Klebsiella spp. infections is essential. Here, we used high-throughput DNA sequencing to characterize the milk microbiota of healthy dairy cows (HDCs) and cows with a history of recurrent Klebsiella mastitis (KLB). Our goal was to identify potential pathogenic genera associated with recurrent Klebsiella infections in cows. Relative abundances of Firmicutes and Faecalibacterium were greater in the KLB group than in the HDC group. In contrast, Proteobacteria and Labrenzia were less abundant than they were in the HDC group. Although species distributions differed between groups, diversity and abundance of communities were comparable. Notably, genera enriched in the KLB group were mostly associated with the intestine, which suggests that cows in the KLB group resided in a contaminated environment or had increased teat-end exposure to fecal bacteria. There were no major differences in microbiota among quarters or between foremilk and milk collected after foremilking. Conversely, the milk of heifers had increased alpha diversity compared to the milk of multiparous cows.
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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 | 0.000 |
| 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".