The Composition of the Bacterial Community in Raw Milk from Holstein Dairy Cattle Correlated with the Occurrence of <i>Klebsiella pneumoniae</i> Clinical Mastitis Infections
Bibliographic record
Abstract
Abstract Klebsiella pneumoniae is a common, opportunistic bacterial pathogen that can cause severe clinical mastitis in dairy cattle. Optimizing the bovine udder microbiome to resist mastitis pathogens is a growing area of research; however, previous work has not examined which members of the mammary microbiome may have antagonistic interactions with K. pneumoniae . In this study, we collected quarter-level milk samples from Holstein dairy cows in Québec, Canada every two weeks for 14 months and analyzed differences in the milk microbiome between samples that were collected from healthy quarters, quarters that developed subclinical mastitis, and quarters that experienced K. pneumoniae clinical mastitis (KP-CM) ( n = 512 milk samples). The occurrence of subclinical mastitis did not cause significant differences in the alpha-diversity of the milk microbiome, nor did subclinical mastitis alter the interactions between taxa in the microbiome. However, the occurrence of KP-CM caused reductions in Shannon diversity in raw milk relative to healthy milk and altered the interactions between taxa. Specifically, K. pneumoniae showed negative interactions with the genus Aerococcus. The negative interactions between Aerococcus spp. and K. pneumoniae in the context of the bovine milk microbiome should be analyzed further.
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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 | 0.001 | 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".