Subclinical mastitis and associated risk factors on dairy farms in New South Wales
Bibliographic record
Abstract
OBJECTIVE: To determine the current prevalence of subclinical mastitis (SCM) and associated risk factors on dairy farms in New South Wales. METHODOLOGY: A survey was sent to 382 dairy farmers to acquire information on the relevant risk factors associated with SCM. RESULTS: The average herd prevalence of SCM among the 189 respondents (response rate 49.5%) was 29%. Farmers who had herds with a low prevalence (<20% cows with individual somatic cell count (ISCC) >2 × 10⁵ cells/mL) more frequently wore gloves during milking (26% vs 62%), used individual paper towels for udder preparation (16% vs 62%), fed cows directly after milking (47% vs 87%) and more frequently treated cows with high ISCC (69% vs 80%) than farmers who had herds with a high prevalence of SCM (>30% cows with ISCC >2 × 10⁵ cells/mL). The latter more often used selective dry cow therapy (52% vs 24%), compared with low prevalence herds. CONCLUSION: The prevalence of SCM in this cross-sectional study is comparable or lower than reported in other studies from North America and the European Union. The outcome provides a benchmark for the current focus of the NSW dairy industry on the management practices associated with a low prevalence of SCM, such as wearing gloves, using paper towels and feeding cows directly after milking.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".