Conformation traits of Holstein cows and their association with a Canadian economic selection index
Bibliographic record
Abstract
Pro$ is a Canadian economic selection index aimed to maximize profit by increasing production yields, while maintaining conformation and functional traits. Currently, there is an interest in understanding the individual contributions of conformation traits recorded in Canada to the overall economic value of a cow and whether they are equally important. We used multiple polynomial regression and principal component analysis to assess the association of 26 conformation traits with Pro$ using relative breeding values (RBVs) from 9351 proven bulls. The best reduced regression model explained 72.5% of the variance in Pro$, with heel depth and body depth having the highest and lowest effect on Pro$ values, respectively. Four traits classified as intermediate optimum traits, e.g., teat length, showed significant linear association with Pro$ instead of quadratic, whereas bone quality was not significantly associated with the index. Principal component analysis indicated that highly profitable bulls share similar RBV, with a subclustering of bulls of daughters with better mammary system versus better dairy strength and feet and legs. These results provide understanding of the individual contributions of conformation traits to Pro$ and give information to guide the Canadian dairy industry on how to best consider these traits in recording and genetic evaluation programs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".