Feasibility of high immune response (HIR) technology as a health management tool to characterize immune response profiles of dairy cattle
Bibliographic record
Abstract
High Immune Response (HIR) is a patented evaluation technology that has the potential to improve the health and food quality of dairy cattle through the reduction of antibiotics and enhanced resistance to economically important diseases such as mastitis. The test includes a blood sample to evaluate antibody-mediated immune response (AMIR) and a skin thickness measurement to evaluate cell-mediated immune response (CMIR). Dairy cattle with a high immune response to test antigens are at a lower risk for developing disease compared to average and low responding animals. Focus groups conducted in two Ontario dairy regions indicated significant interest in HIR (75% of producers) for culling, grouping, treating, and breeding animals. Pre-commercialization activities are underway to conduct: (1) a quantitative market assessment of interest in HIR throughout Ontario (n=128 producers, 3% of Ontario herds) to confirm qualitative focus group data; (2) HIR testing of Ontario AI cull-sires as an application of HIR and to demonstrate no cross-reactivity with governmental health testing; (3) a validation study of previous research to rank cattle based on antibody from milk in lieu of blood (n=21 cows); and (4) beta-testing of HIR on one to two Ontario dairy herds to demonstrate the economic value of HIR (n=250-350 animals per herd). Knowledge transfer and communication research of the HIR technology are also being conducted by attending dairy symposia throughout Ontario to speak to producers and veterinarians, to provide information media, and to recruit participants for educational workshops about HIR.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".