Milk yield and quality at cow udder quarter level as influenced by quarter position, pathogen and somatic cell score
Bibliographic record
Abstract
Few information on milk from complete draining of individual udder quarters is currently available. The aim of the study was to assess how and to which extent milk yield and quality at udder quarter level are influenced by quarter position, pathogen and somatic cells. Quarter milk samples (n=120) of 10 Simmental cows were collected in three consecutive sampling days. Milks were analysed for bacteriology, chemical composition (fat, protein, casein and lactose, %), pH and urea content (mg/dL). Somatic cell count (SCC) and differential SCC were also determined. Data were analysed with a linear mixed model which included the fixed effects of quarter position (right front, left front, right rear, left rear), pathogens (presence or absence) and somatic cell score (SCS) (4 classes, defined on quartiles of SCS distribution), and the random effects of cow nested within quarter level and residual. Quarter position significantly affected milk yield (p < 0.05), with rear quarters being the most productive. Pathogens had a negligible effect on milk yield and quality. Somatic cell score was significant in explaining the variability of fat, lactose, DSCC and pH (p < 0.05).
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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 | 0.000 | 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".