Development of optimal genetic evaluations for teat and udder structure in Canadian Angus cattle
Bibliographic record
Abstract
Despite their heritability and influence on female productivity, there are currently no genetic evaluations for teat and udder structure in Canadian Angus cattle. The objective of this study was to develop optimal genetic evaluations for these traits in the Canadian Angus population. Guidelines recommended by Beef Improvement Federation (BIF) were used to score teat and udder structure in 1,735 Canadian Angus cows from 10 representative herds. Cows scored ranged in parity from 1 to 13; however, >70% of cows were parity ≤4. Scores ranged from 1 (large, bottle shaped) to 9 (very small) for teats and from 1 (very pendulous) to 9 (very tight) for udders. Consistent with parity distribution, >70% of teat and udder scores were ≥6. Teat and udder scores (TS9 and US9, respectively) were modeled using a multiple trait animal model with random effects of contemporary group (herd-year-season) and additive genetic effect, and fixed effects of breed, parity group, and days between calving and scoring. To test good versus poor structure, a binary classification of 1 or 2 (TS2, US2) [comprised of scores 1 to 5 = 1 (poor structure) and scores 6 to 9 = 2 (good structure)] was created. Further, to assess the impact of grouping less frequently observed poor scores, a 1 to 7 scale (TS7, US7) was created by combining teat and udder scores 1 to 3. Analyses for teat and udder scores on scales TS9, US9, TS7, US7, and TS2, US2 were compared. In addition, both threshold and linear animal models were used to estimate variance components for the traits. Data treatment and models were evaluated based on correlation of resulting estimated breeding value (EBV) with corrected phenotypes, Spearman's rank correlation coefficient, average EBV accuracies (r), and deviance information criteria (DIC). TS9, US9 scales for teat and udder scores and linear models performed best. Estimates of heritability (SE) for teat and udder score were 0.32 (0.06) and 0.15 (0.04), respectively, indicating these traits were moderately heritable and that genetic improvement for teat and udder scores was possible. Estimates of phenotypic and genotypic correlations for teat and udder score were 0.46 (0.02) and 0.71 (0.09), respectively. Estimates of genotypic correlations with birth weight (BW), weaning weight (WW), and yearling weight (YW), ranged from -0.04 (0.10) to -0.20 (0.12), verifying the importance of selecting for improved teat and udder score as individual traits, alongside performance traits.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".