Development of a health subindex for genetic selection of bulls and cows in Canadian dairy operations
Bibliographic record
Abstract
This study presents the development of an updated health subindex (HSI) for genetic selection in Canadian Holstein dairy cattle, aimed at enhancing the genetic progress for health-related traits. The HSI was independently designed as part of a broader revision of the Lifetime Performance Index (LPI) and includes mastitis resistance (MR), metabolic disease resistance (MDR), fertility disorders (FD), hoof health (HH), and calf health (CH). The index was constructed by calculating trait-specific economic values, a transformation to relative breeding value units, and index testing using a dataset of 830 Holstein bulls with high genetic reliability. Economic weights were derived by integrating biological and economic data, including veterinarian examination costs, treatment costs, milk loss, and labor, and then adjusted for discounted genetic expressions to reflect trait expression timing and frequency. The final HSI showed moderate correlation with the previous health and fertility subindex (r = 0.61) and low correlation with other LPI components, indicating its distinct selection focus. The final percent emphasis of each trait in the HSI is 36.3% for MR, 31.9% for CH, 12.3% for MDR, 9.8% for FD, and 9.6% for HH. Selection response analysis revealed significant genetic gains in MR and CH when using the HSI for selection, demonstrating the subindex's potential to improve health outcomes in dairy herds. The HSI offers Canadian dairy producers a targeted tool for breeding healthier Holsteins, supported by updated economic and biological data and industry feedback.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".