Genetic Evaluation for Resistance to Metabolic Diseases in Canadian Dairy Breeds
Bibliographic record
Abstract
Genetic evaluation was developed for resistance to metabolic disease traits in Canadian Ayrshire, Holstein and Jersey breeds, with the first official release scheduled for December 2016. The model is a 9-trait animal linear model including producer-recorded data on clinical ketosis ( CK ) and displaced abomasum ( DA ), sub-clinical ketosis ( SCK ) defined as a level of milk β-hydroxybutyrate, and 2 indictor traits: fat to protein ratio ( F:P ) and first lactation body condition score ( BCS ) from the conformation classification. First and later (up to the 5 th ) lactations are considered as different (but correlated) traits. Genetic parameters were estimated using a subset (records on 35,575 cows) of the Holstein data. Heritabilities for CK and DA ranged from 0.02 to 0.06. Higher heritabilities were estimated for SCK and indicator traits, from 0.08 (SCK in later lactations) to 0.30 (BCS). Genetic correlations of clinical disease traits between first and later lactations were strong (0.70 for CK and 0.79 for DA), correlations for SCK and F:P were 0.50 and 0.70, respectively. First lactation CK was strongly correlated with DA (0.77) and SCK (0.68); lower correlations were estimated with BCS (-0.56) and F:P (0.42). Genetic links between DA in first and lactations and indicator traits were weaker. EBVs for CK, DA and SCK are published as relative breeding values, with a mean of 100 and standard deviation of 5, where higher values are desirable. The overall Metabolic Disease Resistance ( MDR ) index includes SCK, CK and DA, with weights of 50%, 25% and 25%, respectively, and the component EBVs are the averages of first and later lactation EBV for each trait. The MDR index is standardized in the same way as EBVs for individual metabolic disease traits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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; both teacher heads agree on what is shown here.
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".