Differences in milk microbiota between healthy cows and cows with recurring <i>Klebsiella</i> mastitis
Bibliographic record
Abstract
Abstract Klebsiella spp. infections continue to have a significant economic impact on the dairy industry, being an important cause of severe clinical mastitis, recurrent infections, and demonstrating poor response to antimicrobials. It is, therefore, essential to investigate the underlying causes of Klebsiella spp. infections. Here we used high-throughput DNA sequencing to characterize the milk microbiota of healthy dairy cows (HDC) and cows with history of recurrent Klebsiella mastitis (KLB). Our goal was to identify potential pathogenic genera associated with recurrent Klebsiella infections in cows. The relative abundance of Firmicutes and Faecalibacterium was greater in the KLB group than in the HDC group. In contrast, Proteobacteria and Labrenzia were less abundant than in the HDC group. Although the species distributions differed between groups, diversity and abundance of communities were comparable. Notably, genera of increased occurrence in the KLB group were mostly intestinal-associated, which suggests that cows in the KLB group resided in a contaminated environment or had increased teat-end exposure to fecal bacteria. We did not detect major differences in microbiota among quarters, and also between fore-strip milk and milk collected after fore-stripping. Conversely, milk of heifers had increased alpha diversity in comparison to 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 | 0.000 | 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".