Phenotypic and Genetic Influences on Test-Day Measures of Acetone Concentration in Milk
Bibliographic record
Abstract
The objectives of this study were to estimate heritability of acetone concentration in milk, based on monthly samples of milk obtained as part of a routine milk testing program, and to evaluate the feasibility of using such data in a genetic evaluation program for selection against ketosis incidence. Milk samples were collected from January to December of 1999 in herds enrolled in the Ontario Dairy Herd Improvement Association, and acetone concentration was measured using an inline chemical procedure. The original data included more than 50,000 records. Because ketosis is generally a problem during early lactation, only the single test with the fewest days in milk was retained. In addition, data were retained only for cows with pedigree information. The final data set included 10,375 records. Among these data, only 6.56% had detectable levels of acetone. Acetone data were log-transformed prior to statistical analysis. Simple ANOVA indicated that herd, parity number, days in milk, and month of test had significant effects on acetone concentration. Acetone levels increased with lactation number and were higher in early lactation. Three approaches were applied for genetic analysis. First, REML was used with a simple linear animal model. Then, a separate procedure used data augmentation and Gibbs Sampling to obtain continuously distributed underlying values for records with zero acetone concentration, and these data were analyzed with both an animal and sire model. Heritability of acetone concentration was less than 1% for all 3 analyses. Herd effects accounted for about 5% of the phenotypic variance. Low estimates of heritability were due either to low actual levels of genetic variance or inability to detect all of the genetic variance present, due to infrequent recording during the early part of the lactation. Genetic evaluation based on recording of acetone concentration on a monthly basis seems of little use as a selection tool to decrease incidence of ketosis.
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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 | 0.003 |
| 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".