Effect of coagulase-negative staphylococci on somatic cell count in Dutch dairy herds
Bibliographic record
Abstract
The effect was quantified of coagulase-negative staphylococci (CNS) intramammary infections on quarter- and cow-level somatic cell count (SCC) and on bulk milk somatic cell count (BMSCC) in different BMSCC cohorts in Dutch dairy herds. Two datasets were used for this purpose. In the first dataset, on 49 randomly selected dairy farms a total of 4220 quarter milk samples of 1072 cows were collected of all cows and heifers with a test-day SCC 250 000 and 150 000 cells/ml, respectively, and of 25% of cows and heifers below these thresholds. In the second dataset, on 39 selected dairy farms a total of 8329 quarter milk samples of 2115 cows were collected of all cows with a test-day SCC 250 000 cells/ml following two consecutive SCC <250 000 cells/ml, and of heifers using the same SCC criteria but with a threshold of 150 000 cells/ml. These cows and heifers were defined as new high SCC. In both datasets, CNS was the most frequently isolated pathogen, 11% in the first dataset and 12% in the second dataset. In both datasets, quarters with CNS IMI had a lower SCC than quarters infected with major pathogens, and a higher SCC than culture-negative quarters. The same was found for SCC at cow level. Coagulase-negative staphylococci were more often found in quarters with SCC 200 000 cells/ml in dairy farms with a BMSCC <150 000 cells/ml compared with dairy farms with a higher BMSCC. Prevalence of CNS in cows and heifers with a high SCC was higher in dairy farms with a BMSCC <150 000 cells/ml compared with dairy farms with a medium or high BMSCC: 30, 19 and 18%, respectively. This indicates that CNS IMI as a cause of subclinical mastitis is relatively more important in dairy farms with a low BMSCC and may become a point of attention in udder health management on that type of farm.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".