Reliability of the bulk milk somatic cell count as an indication of average herd somatic cell count
Bibliographic record
Abstract
Bulk milk somatic cell count (BMSCC) is a frequently used parameter to estimate the subclinical mastitis prevalence in a dairy herd, but it often differs considerably from the average SCC of all individual cows in milk. In this study, first the sampling variation was determined on 53 dairy farms with a BMSCC ranging from 56 000 to 441 000 cells/ml by collecting five samples on each farm of the same bulk tank. The average absolute sampling variation ranged from 1800 to 19 800 cells/ml. To what extent BMSCC represents all lactating cows was evaluated in another 246 farms by comparing BMSCC to the average herd SCC corrected for milk yield (CHSCC), after the difference was corrected for the sampling variation of BMSCC. On average BMSCC was 49 000 cells/ml lower than CHSCC, ranging from -10 000 cells/ml to 182 000 cells/ml, while the difference increased with an increasing BMSCC. Subsequently, management practices associated with existing differences were identified. Farms with a small (<20%) difference between BMSCC and CHSCC administered intramuscular antibiotics for the treatment of clinical mastitis more often, used the high SCC history when cows were dried off more frequently and had a higher number of treatments per clinical mastitis case compared with farms with a large (20%) difference. Farms feeding high-SCC milk or milk with antibiotic residues to calves were 2.4-times more likely to have a large difference. Although sampling variation influences the differences between BMSCC and CHSCC, the remaining difference is still important and should be considered when BMSCC is used to review the average herd SCC and the subclinical mastitis prevalence.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".