Milk yield and quality at udder quarter level are influenced by quarter position, pathogen, and somatic cell score
Bibliographic record
Abstract
There is a paucity of information about milk composition and quality at the quarter level from complete milking of the udder, mainly because of the difficulties of sampling procedures which imply the adoption of a vacuum system connected to four independent buckets. The objective of this study was to evaluate the effect of quarter position, intramammary infection status, and somatic cells on milk yield and composition at the mammary quarter level of Simmental cows. Milk samples were collected during three consecutive days, after milking individual mammary quarters of 10 lactating cows separately, for a total of 120 quarter milk samples. Samples were analysed for composition, somatic cell count (SCC), differential SCC, and bacterial culture. Somatic cell count was log-transformed to somatic cell score (SCS) to achieve normality of distribution. Sources of variation of milk yield and quality traits were investigated using a linear mixed model which included the fixed effects of quarter position (1 to 4), pathogen presence (positive or negative), and SCS class (low, medium-low, medium-high, and high). The interaction between cow and quarter position was included as random effect. Quarter position significantly affected milk yield, with rear udder quarters yielding more milk compared to their front counterparts, but with negligible effects on milk composition. Classes of SCS influenced milk composition, in a way that quarters with high SCS had lower lactose content and higher differential SCC. This study is a contribution towards the possibility to better understand physiological processes at cow udder quarter level.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".