Investigation of genotype × country interactions for growth traits in beef cattle
Bibliographic record
Abstract
The importance of genotype x country interactions for weaning and birth weight and postweaning gain between Argentina (AR), Canada (CA), Uruguay (UY), and the United States (US) for populations of Hereford cattle was investigated. Three sample data sets of computationally manageable sizes were formed for each trait and pairwise combination of countries to investigate possible interactions. Parameters were estimated for each sample data set via an accelerated EM-REML algorithm and multiple-trait animal models that considered either weaning or birth weight as a different trait in each country. Direct and maternal (in parentheses) weaning weight genetic correlation estimates for AR-CA, AR-UY, AR-US, CA-UY, CA-US, and UY-US were 0.82 (0.80), 0.81 (0.72), 0.81 (0.79), 0.83 (0.78), 0.85 (0.82), and 0.86 (0.81), respectively. Direct and maternal (in parentheses) birth weight genetic correlation estimates were 0.92 (0.62), 0.97, (0.85), and 0.99 (0.97) for AR-CA, AR-US, and CA-US, respectively. Birth weight was not analyzed for UY due to small amounts of data. Postweaning gain in CA and US was 160-d gain, and in AR and UY 345-d gain was used. Across-country direct genetic correlations for postweaning gain were estimated for each pairwise country data set using a model that considered weaning weight as the same trait across each country, whereas postweaning gain was treated as a different trait in each country. Direct genetic correlation estimates for postweaning gain for AR-CA, AR-UY, AR-US, CA-UY, CA-US, and US-UY were 0.64, 0.80, 0.51, 0.84, 0.92, and 0.83, respectively. The overall results indicate that weaning and birth weights of Hereford calves can be analyzed as the same trait in all countries with a common set of heritabilities and genetic correlations, after adjustment for heterogenous phenotypic variances across countries. Postweaning gain in CA and US can be considered as the same trait and analyzed using a single set of parameters. Postweaning gain in AR and UY should be considered as a separate trait from postweaning gain in CA and US, and postweaning gain in AR and UY can be considered as the same trait and analyzed using a common heritability, after adjustment for phenotypic variance differences between the two countries.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".