Presence of viral and bacterial organisms in milk and their association with somatic cell counts
Bibliographic record
Abstract
About 20 to 35% of milk samples from cows with intramammary infection or high somatic cell count (SCC) are negative on bacteriological culture analysis. However, little is known about SCC in milk of cows infected with viruses. In the first part of our study, we developed a real-time PCR assay for detection of bovine herpesvirus (BHV) 1, BHV2, and BHV4, and bovine viral diarrhea virus (BVDV) in composite quarter milk samples. A total of 1,479 lactating cows of 1,964 cows in the dairy herd were initially selected because these cows had complete SCC data for at least 3 consecutive test results, of which 139 lactating cows from different lactation age groups were selected randomly and studied extensively. Composite quarter milk samples were collected on 3 alternate days and examined for viruses, SCC, and bacteriological analysis. In total, 10, 28, and 0.7% of the composite quarter milk samples from cows were positive for BHV1, BHV2, and BHV4, respectively; BVDV was not detected in composite quarter milk samples. Bovine herpesvirus was not associated with a particular bacterial species. Our study results indicate that cows positive for BHV in composite quarter milk samples alone are less likely to have elevated SCC compared with cows with bacterial intramammary infection; BHV1, BHV2, and BHV4 are probably not major udder pathogens.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".