Genetic variability of health disorders in Ontario Holstein cows
Bibliographic record
Abstract
Extensive emphasis on selection for milk yield with minimal attention to the animals' functional performance has increased the yield of North American dairy cattle. The high intensity of selection for production traits such as milk yield, protein yield and fat yield has also brought about a rapid increase in genetic relationships among animals. In dairy cattle, correlated response to selection for milk yield includes fertility and susceptibility to diseases. Although the high producing cows have greater net profit, they also have higher mammary and discarded milk costs associated with high production. Long-term genetic selection against clinically diagnosed diseases might be useful to diminish their incidence. The Scandinavian countries have incorporated the health traits into their selection indices. Canadian breeding programs realize the need to consider traits other than the yield in selection decisions. The objective of this study was to determine the genetic variability of various clinically diagnosed health traits. Data from 171 herds of the Ontario milk-recording program were used. These herds are collaborating with the University of Guelph (Dr. David Kelton) to record health traits. A major impediment to estimating heritabilities for the majority of the disorders was that the progeny group size per sire was not large enough to enable detecting a significant difference among sires. Hence, heritability estimates were not obtained for all the health disorders included in the study. The progeny group size per sire has to be increased such that there are at least 5 cases per progeny group to enable detecting a difference among sires. Heritability estimates for retained placenta and displaced abomasum in the first lactation were 0.067 and 0.212 respectively. The heritability estimate of cystic ovaries in the second lactation was 0.092. In the third lactation, the heritability estimate of mastitis was 0.10 whereas retained placenta had a heritability of 0.25.
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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 | 0.000 | 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 | 0.001 |
| 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".