Day-to-day variation in milk yield and milk composition at the udder-quarter level
Bibliographic record
Abstract
Automatic in-line measurement of milk composition and milk yield could be a useful tool in management of the dairy herd. Data on milk components and milk yield provide information on milk quality alterations and cow health status but are also useful in planning feeding and breeding. In automatic milking systems, udder quarters are milked individually, enabling analysis and recording at the udder-quarter level. Frequent records of components require knowledge about day-to-day variations. A component with greater day-to-day variation needs more frequent sampling when used as a diagnostic tool and for management decisions. Earlier studies have described the day-to-day variations in milk components for cow composite milk, but with the quarter milking technique and the possible sampling at the udder-quarter level, knowledge about day-to-day variations at the udder-quarter level is needed. In this study, udder-quarter and cow composite milk samples were collected from 42 consecutive milkings of 10 cows during 21 d. Milk yield was recorded and the milk was analyzed for total protein, whey protein, casein, fat, lactose, and somatic cell count. The results showed that the day-to-day variations and mean values for 4 healthy udder quarters within a cow were similar. In addition, different milk components had different levels of day-to-day variation, the least variation being found in lactose (0.9%) and the greatest in fat (7.7%). This suggests that repeated milk sampling and analysis at the udder-quarter level can be used to detect alterations in composition and cow health and would, thus, be helpful in the management of the dairy herd.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".