Genetic parameters for producer-recorded health data in Canadian Holstein cattle
Bibliographic record
Abstract
Health traits are of paramount importance for economic dairy production. Improvement in liability to diseases has been made with better management practices, but genetic aspects of health traits have received less attention. Dairy producers in Canada have been recording eight health traits (mastitis (MAST), lameness (LAME), cystic ovarian disease (COD), left displaced abomasum (LDA), ketosis (KET), metritis (MET), milk fever (MF) and retained placenta (RP)) since April 2007. Genetic analyses of these traits were carried out in this study for the Holstein breed. Edits on herd distributions of recorded diseases were applied to the data to ensure a sufficient quality of recording. Traits were analysed either individually (MAST, LAME, COD) or were grouped according to biological similarities (LDA and KET, and MET, MF and RP) and analysed with multiple-trait models. Data included 46 104 cases of any of the above diseases. Incidence ranged from 2.3% for MF to 9.7% for MAST. MET and KET also had an incidence below 4.0%. Variance components were estimated using four different sire threshold models. The differences between models resulted from the inclusion of days at risk (DAR) and a cow effect, in addition to herd, parity and sire effects. Models were compared using mean squared error statistic. Mean squared error favoured, in general, the sire and cow within sire model with regression on DAR included. Heritabilities on the liability scale were between 0.02 (MET) and 0.21 (LDA). There was a moderate, positive genetic correlation between LDA and KET (0.58), and between MET and RP (0.79).
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".