Incidence Rate of Clinical Mastitis on Canadian Dairy Farms
Bibliographic record
Abstract
No nationwide studies of the incidence rate of clinical mastitis (IRCM) have been conducted in Canada. Because the IRCM and distribution of mastitis-causing bacteria may show substantial geographic variation, the primary objective of this study was to determine regional pathogen-specific IRCM on Canadian dairy farms. Additionally, the association of pathogen-specific IRCM with bulk milk somatic cell count (BMSCC) and barn type were determined. In total, 106 dairy farms in 10 provinces of Canada participated in the study for a period of 1 yr. Participating producers recorded 3,149 cases of clinical mastitis. The most frequently isolated mastitis pathogens were Staphylococcus aureus, Escherichia coli, Streptococcus uberis, and coagulase-negative staphylococci. Overall mean and median IRCM were 23.0 and 16.7 cases per 100 cow-years in the selected herds, respectively, with a range from 0.7 to 97.4 per herd. No association between BMSCC and overall IRCM was found, but E. coli and culture-negative IRCM were highest and Staph. aureus IRCM was lowest in low and medium BMSCC herds. Staphylococcus aureus, Strep. uberis, and Streptococcus dysgalactiae IRCM were lowest in the Western provinces. Staphylococcus aureus and Strep. dysgalactiae IRCM were highest in Québec. Cows in tie-stalls had higher incidences of Staph. aureus, Strep. uberis, coagulase-negative staphylococci, and other streptococcal IRCM compared with those in free-stalls, whereas cows in free stalls had higher Klebsiella spp. and E. coli IRCM than those in tie-stall barns. The focus of mastitis prevention and control programs should differ between regions and should be tailored to farms based on housing type and BMSCC.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".