Models for Genetic Evaluation of Scrotal Circumference in Red Angus
Bibliographic record
Abstract
Growth and scrotal records were obtained to estimate age of dam and age at measurement adjustment factors for yearling scrotal circumference in Red Angus bulls (n = 40,865), and to estimate genetic parameters. The linear partial regression coefficient for age at measurement was 0.0323 cm/d. The recommended factors were 0.70, 0.37, 0.08, and 0.30cm to adjust the scrotal circumference records of yearling Red Angus bulls out of 2-, 3-, 4-, and ≥ 10-yr old cows to a mature (5 to 9 yr) age of dam equivalent. Adjusted 365-d scrotal circumference (SC365) records were fitted to animal models including direct genetic and from 0 to 3 maternal effects; however, maternal effects were negligible. Her-itability for SC365 was 0.51 ± 0.02 from a model including only direct genetic effects. Bivariate models were used to estimate parameters for SC365 with birth (BWT) and 205-d BW, and 160-d postweaning gain (PWG). Estimated genetic correlations (± 0.03) were 0.10, 0.13, and 0.13 for SC365 with direct effects on BWT, 205-d BW, and PWG, respectively. Maternal BWT had a low genetic correlation (0.05 ± 0.05) with SC365, but the genetic correlation between maternal 205-d BW and SC365 was moderate (0.35 ± 0.04). In addition to the recommendations for adjustment of scrotal circumference, these results suggest that direct genetic effects on SC365 could be used in selection programs, and that increasing selection for SC365 would be concomitant with growth up to yearling age and not antagonistic to maternal ability in Red Angus.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".