Cross-sectional study of Streptococcus species in quarter milk samples of dairy cows in the canton of Bern, Switzerland
Bibliographic record
Abstract
A total of 2538 quarter milk samples from 638 lactating dairy cows from 47 farms in the canton of Bern, Switzerland, were investigated for streptococci. A novel, simple and inexpensive laboratory method was used for the differentiation of Streptococcus species, and a risk factor analysis was carried out. The prevalence in the quarter milk samples was 0.2 per cent for Streptococcus agalactiae, 1.3 per cent for Streptococcus uberis, 1.3 per cent for Streptococcus dysgalactiae, 0.1 per cent for Enterococcus species and 2.9 per cent for minor Streptococcus species (designated Streptococcus-Lactococcus-Enterococcus [SLE] group). Based on the somatic cell count (SCC), S uberis and S dysgalactiae were classified as 'major' pathogens and the bacteria in the SLE group as 'minor' pathogens. For S uberis, S dysgalactiae and bacteria in the SLE group, the most significant risk factor was an intramammary infection (IMI) of a neighbouring quarter by the same pathogen. Other significant risk factors for S uberis infection were a positive California Mastitis Test (CMT) result and a SCC of more than 100,000 cells/ml. Significant risk factors for IMI with S dysgalactiae were a positive CMT result, teat injury and palpable abnormalities in the udder. Infection with bacteria in the SLE group was significantly associated with a SCC of more than 100,000 cells/ml, a lactation number of more than 2, the right rear quarter (as the location of infection) and a positive CMT result.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".