Comparison of models and impact of missing records on genetic evaluation of calving ease in a simulated beef cattle population
Bibliographic record
Abstract
This study compared the application of a bivariate linear-linear (LL) and a linear-threshold (LT) sire-maternal grandsire model for genetic evaluation of calving ease (CE), using birth weight (BW) as a correlated trait, and assessed the impact of missing records on genetic evaluation of CE in a simulated multi-breed beef population that mimicked phenotypic and genetic parameters of beef cattle in Ontario. Models included fixed age-of-dam by sex-of-calf, management group, breed and heterosis effects, and random direct and maternal genetic, maternal permanent environment and residual effects. The LL model was applied to BW and CE Snell scores, and LT model was applied to BW and CE raw scores. CE evaluations were similar between the LL and LT models with no obvious advantage for either model. The two models performed similarly with respect to accuracy and rank correlation of predicted genetic effects and recovered true values of genetic parameters and fixed effects, except for CE maternal heterosis from LL model. The effect of missing records was assessed using the LT model. All dispersion and location parameters were generally well recovered, even when the total proportion of missing records of both traits was up to 41%. Levels of missing CE and BW records that exist in Ontario do not seem to adversely affect genetic evaluation of CE. Key words: Accuracy, Gibbs sampling, heterosis, Snell score
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".