Genetic evaluation of dairy cattle for conformation traits using random regression models
Bibliographic record
Abstract
Summary Random regression models were applied to eight conformation traits (i.e. stature, rump angle, thurl width, rear leg set, rear udder width, rear udder height, udder depth, and fore udder attachment) of Holstein cows from the northeastern United States. Covariates for fixed and random regressions included age and age‐squared for six of the traits, and two additional covariates were included for rear udder width and rear udder height. Other effects in the model were herd—year‐classifier and months in milk. Fixed covariates were nested within year of birth of the cow. Variance components were estimated using Bayesian theory and Gibbs sampling procedure. Estimated breeding values from the random regression models were compared to two single trait models. The first model utilized only the first classification record of the cow in first lactation, and the second model utilized all classifications of the cow in a simple repeatability model. Additive genetic merit for conformation traits changed with the age of the animal. Some traits were affected by age more than others. The single trait, single record model and the simple repeatability model were not appropriate in predicting breeding values at mature ages for rear udder width and rear udder height.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".