Quarter milking - a possibility for detection of udder quarters with elevated SCC
Bibliographic record
Abstract
One of the most common diseases in dairy production is mastitis. Clinical mastitis is easily detected and usually milk composition is changed. Subclinical mastitis often remains undetected, since there are no clinical signs of inflammation and the milk appears normal when inspected. The aim of this study was to find out whether a moderate increase in somatic cell count (SCC) is associated with measurable changes in milk composition and milk yield when analysed on individual udder quarters and comparisions are made with the opposite healthy quarter. During 13 weeks, 4158 bulk quarter milk samples from 68 cows were collected twice weekly and analysed for milk composition and SCC. Milk yield was registered at udder quarter level. For calculations, three groups of cows were formed according to their SCC value. Group 1 cows, where all quarters had a SCC <100 000 cells/ml, were considered to be unaffected. Group 2 cows had one udder quarter with a SCC >100 000 cells/ml and 1.5-fold higher than the opposite quarter at one sampling occasion. For group 3 cows, the increase remained for more than one consecutive sampling occasion. Data from group 1 cows revealed that front and rear quarters were similar when compared to each other. For both groups 2 and 3 cows, the lactose content in milk decreased statistically significant simultaneously with the increase in SCC in the affected quarter. For group 3 cows, the levels remained for two sampling occasions after the initial increase in SCC. It was concluded that deviations in lactose content within pairs of front and rear quarters, respectively, may be a useful tool for detection of a moderate increase in SCC in separate udder quarters.
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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 | 0.000 |
| 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".