A Genome-Wide Association Study for Immune Response Traits in Canadian Holsteins
Bibliographic record
Abstract
The inclusion of immune response traits in breeding indices has been suggested to improve inherent disease resistance in dairy cattle (Abdel-Azim et al., 2005; Mallard et al., 2011). Using the High Immune Response Technology developed at the University of Guelph, cows with superior cell-mediated (CMIR) and antibody-mediated immune responses (AMIR) can be identified (Wagter and Mallard, 2007). Holstein cows classified as High AMIR have been shown to have lower occurrence of mastitis in 2 out of 3 herds tested, improved response to commercial vaccine and increased milk and colostrum quality (Wagter et al., 2000). High immune response (HIR) cows have also been shown to have decreased incidence of diseases such as mastitis, metritis, ketosis and retained placenta (DeLapaz, 2008; Thompson-Crispi et al., 2012a). These previous studies found many benefits to identifying HIR cows in a herd, however, they were performed on one or a few herds in a single region.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".