Prevalence and distribution of mastitis pathogens in subclinically infected dairy cows in Flanders, Belgium
Bibliographic record
Abstract
The main objective was to determine the prevalence of intramammary infections (IMI) in dairy cows in Flanders, Belgium. Data were obtained from quarter milk samples of dairy herds subjected to a mandatory yearly screening of all lactating cows. A total of 178,668 quarter milk samples were collected at 1087 cross-sectional dairy herd screenings performed in three consecutive years. Of the dairy cows, 40% had at least one culture-positive quarter. More than 50% of all IMI were caused by non-aureus staphylococci. Streptococcus agalactiae is almost eradicated in Flanders, whereas Staphylococcus aureus was isolated from 18% of the culture-positive quarters. In addition, the distribution of mastitis pathogens in quarter milk samples from selected dairy cows with an elevated somatic cell count (SCC) is described. From 6390 cows with a geometric mean composite SCC 250,000 cells/ml, nearly 65% had at least one culture-positive quarter. The majority of the IMI were caused by non-aureus staphylococci (41.1%), whereas Staph. aureus and aesculin-positive cocci were found in respectively 25% and 18% of the culture-positive milk samples. We conclude that more efforts are needed in the prevention and control of subclinical mastitis in Flanders. Non-aureus staphylococci are the predominant cause of IMI, warranting more research regarding the epidemiology and pathogenicity of those species.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 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".