Genetic parameters for calving ease, gestation length, and birth weight in Charolais cattle1
Bibliographic record
Abstract
In this study, a 3-trait linear model was used to obtain genetic parameters for direct and maternal components of calving ease (CE), gestation length (GEST), and birth weight (BWT). Calving ease scores were transformed into Snell scores and expressed as percent unassisted calving (SC), ranging from 0 to 100% (least to greatest ease). A total of 40,420 records (n = 14,403 for CE) were obtained from the Canadian Charolais Association field database. The animal model included fixed effects of contemporary group (herd x year of birth combinations), age of heifer, and sex of calf (only for CE), whereas random effects included direct and maternal genetic effects, residual error, and permanent environmental effects (for CE). The BWT and GEST were preadjusted for age of dam and sex of calf effects. Variance components were estimated using REML. Mean SC was 83.31% (SD = 23.30) and ranged from 3.44 to 100%. Mean BWT was 46.54 kg (SD = 4.79), whereas mean GEST was 286.48 d (SD = 4.93). Direct heritability estimates for SC, BWT, and GEST were 0.14 +/- 0.02, 0.46 +/- 0.03, and 0.62 +/- 0.04, respectively, and maternal heritability estimates were 0.06 +/- 0.02, 0.14 +/- 0.02, and 0.10 +/- 0.02, respectively. The permanent environmental effect as a proportion of SC phenotypic variance was 0.35 +/- 0.11, indicating a large influence on CE. Genetic correlations of direct SC with direct BWT and GEST were -0.93 +/- 0.04 and -0.38 +/- 0.08, respectively, whereas maternal correlations were -0.69 +/- 0.14 and -0.49 +/- 0.17, respectively, illustrating the importance of including both traits in CE evaluations. Within trait direct x maternal genetic correlations were substantial and negative. Regression of average direct and average maternal EBV on year of birth yielded significant genetic trends for the direct effects of BWT, GEST, and CE, whereas no trends were detected for maternal effects. Even though CE is routinely analyzed, no study has evaluated transformed CE scores with 2 correlated traits. In these data, the large negative genetic correlation between BWT and CE suggests that increasing SC would also decrease BWT. Genetic improvement programs, therefore, should consider both CE and growth.
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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".