Associations between Canadian Holstein Dairy Cattle Health and Production Traits
Bibliographic record
Abstract
A multiple trait, random regression, test day (TD) model was used to determine the heritability and genetic correlations of milk, fat, protein, and SCS with metritis, cystic ovary disease, mastitis, lameness, ketosis, milk fever, and displaced abomasum for four periods in lactations 1, 2, and 3-5 using two disease definitions. Definition 1 assumed that the first disease occurrence affects all TD thereafter equally and definition 2 assumed that disease affects only the next TD. Results were similar between definitions. The models were modified to allow the estimation of differences in disease occurrence by level of production, and to estimate production differences between diseased and non-diseased cows. Few significant associations were found between production levels and disease occurrence, the largest was between MAST and SCS. Diseased animals had lower levels of production than healthy animals for all diseases except COD. Animals with MAST had increased SCS.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".