Genetic association between cows’ stayability to three years of age and juvenile growth traits in Canadian Angus herds
Bibliographic record
Abstract
Bivariate linear animal models were fit using MTDFREML programs for the analysis of cows’ stayability to 3 yr (STAY3, n = 1, 703) as a binary scored trait paired with body weights at birth (BW, n = 6,116), 205-d weaning (WW, n = 5,360,) and 1 yr of age (YW, n = 5250) in Angus cattle. For STAY3, the model included a fixed effect due to herd ×year of cow’s birth along with a random direct genetic effect. For each of BW, WW and YW, the model included fixed effects due to herd ×birth year, birth season, birth type, calf’s sex and the age of dam (as linear and quadratic covariates), while the random effects were direct and maternal genetic effects and permanent maternal environmental effects. Survival analysis revealed that the risk of cows, 10 yr or less in age, being culled from five Canadian Angus herds was highest between 2 and 3 yr of age. The direct heritability (h2) estimates for BW, WW and YW were 0.54, 0.73 and 0.47, respectively, and corresponding maternal heritability estimates were 0.14, 0.33 and 0.13, respectively. Furthermore, the direct h2 estimate for STAY3 from three bivariate analyses was 0.23–0.24. Estimates of direct-maternal genetic correlations of BW, WW and YW were -0.18, -0.70 and -0.39, respectively. The direct genetic correlations of STAY3 with BW, WW and YW were -0.15 to -0.09 indicating that selection for growth would have less detrimental influence on the stayability trait of cows. The correlations of direct genetic effects of STAY3 with maternal genetic effects of BW, WW and YW were between 0.20 and 0.25, indicating their favorable relationships as correlated traits. Key words: Stayability, growth traits, heritability, genetic correlations, beef cattle
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".