Quarter and cow risk factors associated with a somatic cell count greater than 199,000 cells per milliliter in United Kingdom dairy cows
Bibliographic record
Abstract
Quarter and cow risk factors associated with a somatic cell count (SCC) >199,000 cells/mL at the next milk recording during lactation were investigated during a 12-mo longitudinal study on 8 commercial Holstein-Friesian dairy herds in Southwest England, United Kingdom. The individual risk factors studied on 1,677 cows included assessments of udder and leg hygiene, teat-end callosity and hyperkeratosis, body condition score (BCS), and measurements of monthly milk quality and yield. The outcome variable used for statistical analysis was the next recorded individual cow SCC >199,000 cells/mL. Statistical analysis included use of generalized linear mixed models. Significant covariates associated with an increased risk of SCC >199,000 cells/mL were increasing parity, increasing month of lactation, previous SCC (SCC 200,000 cells/mL and greater, odds ratio = 7.12), and cows with a BCS <1.5 (odds ratio = 2.09) or BCS >3.5 (odds ratio = 2.20). Significant covariates associated with a reduced risk of SCC >199,000 cells/mL were cows with contamination of the skin of the udder and quarters with mild (odds ratio = 0.65) and moderate (odds ratio = 0.62) hyperkeratosis of the teat-end. These results suggest that individual quarter and cow-level factors are important in the acquisition of intramammary infections as measured by SCC during lactation. Cow energy status, as measured by BCS, may influence the risk of intramammary infection during lactation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".