Immuno-phenotyping of Canadian beef cattle: adaptation of the high immune response methodology for utilization in beef cattle
Bibliographic record
Abstract
Abstract The high immune response (HIR) methodology measures the genetic performance of the adaptive immune system to identify and breed animals with balanced and robust immunity. The HIR methodology has previously been used in dairy and swine to reduce disease but has not been fully investigated in beef cattle. The first objective of the current study was to examine whether the HIR methodology as standardized for use in dairy cattle was appropriate for use in beef cattle. The second objective was to determine the earliest age for immune response phenotyping of beef calves. In this study, beef calves (n = 295) of various ages, as well as mature beef cows (n = 170) of mixed breeds, were immunized using test antigens to assess their antibody- (AMIR) and cell-mediated immune responses (CMIR). Heritability for AMIR and CMIR was estimated at 0.43 and 0.18, respectively. The HIR methodology was appropriate for use in beef cattle; beef calves as young as 2–3 wk of age were capable of mounting AMIR responses comparable with those seen historically in mature Holstein dairy cows. Three-week-old beef calves mounted CMIR responses comparable with those of Holstein cows, but 9-mo-old calves and mature beef cows had significantly higher CMIR responses than Holsteins. The HIR methodology can be used to measure both AMIR and CMIR in beef calves as young as 3 wk of age.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".