Genetic parameter estimations and genomic insights for teat and udder structure in young and mature Canadian Angus cows
Bibliographic record
Abstract
Poor teat and udder structure, frequently associated with older cows, impact cow production and health as well as calf morbidity and mortality. However, producer culling, for reasons including age, production, feed availability, and beef markets, creates a bias in teat (TS) and udder scores (US) assessed and submitted to the Canadian Angus Association for genetic evaluations toward improved mammary structure. In addition, due to the infancy of the reporting program, repeated scores are rare. Prior to the adoption of genetic evaluations for TS and US in Canadian Angus cattle, it is imperative to verify that TS and US from young cows are the same traits as TS and US estimated on mature cows. Genetic parameters for TS and US from all cows (n = 4,192) and then from young cows (parities 1 and 2) and from mature cows (parity ≥ 4) were estimated using a single-trait animal model. Genetic correlations for the traits between the two cow age groups were estimated using a two-trait animal model. Estimates of heritability (posterior SD [PSD]) were 0.32 (0.07) and 0.45 (0.07) for young TS and US and 0.27 (0.07) and 0.31 (0.07) for mature TS and US, respectively. Genetic correlation (PSD) between the young and mature traits was 0.87 (0.13) for TS and 0.40 (0.17) for US. Genome-wide association studies were used to further explore the genetic and biological commonalities and differences between the two groups. Although there were no genes in common for the two USs, 12 genes overlapped for TS in the two cow age groups. Interestingly, there were also 23 genes in common between TS and US in mature cows. Based on these findings, it is recommended that producers collect TS and US on their cow herd annually.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".