Short communication: Genetic parameters for measures of calf health in a population of Holstein calves in New York State
Bibliographic record
Abstract
The objectives of this study were to estimate the genetic parameters of preweaning undifferentiated bovine respiratory disease (BRD), umbilical diseases (UMB), and bloat (BLT) for a population of Holstein calves from New York State, as well as to associate the estimated breeding values determined in the current study with traits from ongoing genetic evaluations used in Canada and the United States. Data were recorded for 7,372 heifer calves at a commercial rearing facility in New York State, from arrival at 1 to 7d of age for the duration of stay at the facility. Performance and disease up to weaning and mortality before and after weaning were recorded. The 3 traits of interest, BRD, UMB, and BLT, were scored as 0 or 1 and analyzed using a multivariate linear sire model. The model included fixed effects of arrival weight, serum total protein, weaning weight, and season and year of birth; herd and sire were included as random effects. The heritabilities of the 3 health traits of interest were estimated at 0.09 for BRD, 0.14 for UMB, and 0.04 for BLT. The genetic correlation between the calf health traits BRD and BLT was 0.62. Correlations between BRD and UMB and between BLT and UMB were close to zero. Breeding values were estimated for the 3 calf health traits and correlated with routinely evaluated traits from Canadian and US genetic evaluations (correlations ranged from -0.42 to 0.32). Significant differences existed among Holstein sires for calf health during the preweaning period.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".