Genetic parameters of milk cholesterol content in Holstein cattle
Bibliographic record
Abstract
This study aimed to estimate heritability for milk cholesterol (CHL) and genetic correlations between milk CHL and other production traits (test-day milk, fat, and protein yields, fat and protein percentages, and somatic cell score). Milk CHL content was determined by gas chromatography and expressed as mg of CHL in 100 g of fat (CHL_fat) or in 100 mg of milk (CHL_milk). Univariate models were used to estimate variances and heritability, whereas bivariate models were used to compute correlations using data from 1793 cows. The average concentrations (standard deviation) of CHL_fat and CHL_milk were 275.63 (75) mg and 11.16 (3.63) mg, respectively. Milk CHL content was significantly affected by days in milk and herd (P < 0.05), but not by parity, regardless of the scale of expression. Heritability estimates for CHL_fat and CHL_milk were 0.06 ± 0.04 and 0.17 ± 0.06, respectively. Phenotypic and genetic correlations between CHL_fat and CHL_milk were 0.82 and 0.44 ± 0.24, respectively. CHL_fat had nonsignificant genetic correlations with all production traits, whereas CHL_milk had significant (P < 0.05) genetic correlations with milk yield (−0.47), fat yield (0.51), protein percentage (0.56), and fat percentage (0.88). This is the first study to estimate genetic parameters for milk CHL content. Further studies are required to assess the possibility of genetically selecting cows with lower milk CHL content.
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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".