Health recording in Canadian Holsteins: Data and genetic parameters
Bibliographic record
Abstract
The objective of this study was to investigate if health data recorded by Canadian dairy producers can be used for genetic selection. Eight diseases are recorded by producers on a voluntary basis: mastitis, displaced abomasum, ketosis, milk fever, retained placenta, metritis, cystic ovaries, and lameness. Between 40 to 60% of all herds had to be excluded by editing procedures for each trait, assuming unreliable health recording. All analyses were carried out for first-lactation Holstein cows. The majority of disease cases occurred in the first month of lactation. Mean disease frequencies were 12.6, 3.7, 4.5, 4.6, 10.8, 8.2, and 9.2% for mastitis, displaced abomasum, ketosis, retained placenta, metritis, cystic ovaries, and lameness, respectively. Milk fever was very rare in first-lactation cows with a frequency of only 0.20%, and was, therefore, not considered in the analyses. Univariate and bivariate linear animal models were fitted. Heritabilities for mastitis, displaced abomasum, ketosis, retained placenta, metritis, cystic ovaries, and lameness were 0.02, 0.06, 0.03, 0.03, 0.02, 0.03, and 0.01, respectively. Genetic correlations between diseases were mostly positive. The strongest genetic correlations were found between displaced abomasum and ketosis (0.64) and between retained placenta and metritis (0.62). The remaining genetic correlations ranged from -0.22 (between metritis and lameness) to 0.49 (between mastitis and lameness). In agreement with the genetic correlations, the largest phenotypic correlations were found between displaced abomasum and ketosis (0.27) and retained placenta and metritis (0.14). All other phenotypic correlations were low and close to zero (0.00 to 0.06). Pearson correlations between breeding values for health traits and other routinely evaluated traits were computed, which revealed noticeable favorable relationships to direct herd life and fertility. In addition, a moderate favorable association was found between mastitis and somatic cell score. Mastitis is the most promising trait to be included in routine genetic evaluation, because it is the most recorded disease and has a high frequency and positive genetic correlations to all other health traits. Although, about 40% of all Canadian dairy producers participate in the health-recording system, a large proportion of the data are lost after data validation. Thus, dairy producers should be encouraged to keep accurate and complete health data.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".