Genotype × environment interaction for fertility and milk yield traits in Canadian, Mexican and US Holstein cattle
Bibliographic record
Abstract
The objective of this study was to evaluate genotype × environment interaction (G×E) between Canada, the United States and Mexico for fertility and milk yield traits using genetic correlations between countries estimated from genetic evaluations of sires. Genetic correlation between Mexican and Canadian Holsteins for age at first calving was ≤ 0.48 and lower than the simulated value obtained accounting for data structure and selection effects. For calving interval, genetic correlation between Mexico and Canada ranged from 0.48 to 0.69. Genetic correlation between calving interval in Mexico (multiplied by -1) and daughter pregnancy rate in the United States ranged from 0.64 to 0.73, and was lower than simulated and actual Canada-United States values. Genetic correlations between Mexico and Canada and the United States for milk yield traits were ≥ 0.83, similar to simulated genetic correlations, but lower than Canada-United States values (≥ 0.93). Heritability estimates for age at first calving, calving interval, milk yield, fat yield, protein yield, fat content, and protein content for the Mexican Holstein population were 0.06, 0.03, 0.18, 0.20, 0.19, 0.46, and 0.49, respectively. G×E interaction effects between Canada and Mexico for age at first calving were high, whereas G×E interaction effects between Canada and Mexico for calving interval and between daughter pregnancy rate in the United States and calving interval in Mexico were moderate. G×E interaction effects for milk yield traits between Canada or the United States with Mexico in registered Holsteins were low.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".