Milk losses associated with somatic cell counts per breed, parity and stage of lactation in Canadian dairy cattle
Bibliographic record
Abstract
The reduction in milk production caused by subclinical mastitis in dairy cattle was assessed through the regression of test day milk yield on log-transformed somatic cell counts (LnSCC). Data was obtained from Valacta, Quebec, and a total of 312,756 test day records from Ayrshire cows and 1,869,785 test day records from Holstein cows were included in the analyses. A segmented regression was fitted to estimate the cutoff point in the LnSCC scale where milk yield starts to be affected by mastitis. The statistical model used to explain daily milk yield included the effects of herd–year-season of test (random), days in milk, age at calving and LnSCC, and analyses were performed by breed, parity and stage of lactation. The cutoff point where milk yield starts to be affected by changes in LnSCC was estimated from data to be around 2 (approximately 7400 cells/mL) for Canadian Ayrshires and Holsteins. Milk losses per unit increase in LnSCC varied from 0.55 to 0.84 kg/day in first lactation Ayrshires, from 0.33 to 0.55 kg/day in first lactation Holsteins, from 0.74 to 2.45 kg/day in adult Ayrshires and from 0.77 to 1.78 kg/day in adult Holsteins. Daily milk losses caused by changes in LnSCC were dependent on breed, parity and stage of lactation, and these factors should be considered when estimating losses associated with subclinical mastitis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".