Effect of age and pregnancy status on adaptive immune responses of Canadian Holstein replacement heifers
Bibliographic record
Abstract
Selection for production traits with little or no emphasis on health traits has led to an increase in the incidence of disease in Canadian dairy herds. We describe here a patented protocol for estimating the breeding value for immune responsiveness in heifers that combines measures of both cell-mediated (CM) and antibody-mediated (AM) immune responses (IR). The ability of putative type 1 and type 2 antigens used to induce CMIR and AMIR, respectively, was assessed in replacement Holstein heifers, and the effects of age and pregnancy on type 1 and type 2 IR bias were estimated. Results demonstrated that the type 1 and type 2 antigens induced polarized type 1 and type 2 responses in heifers regardless of age and pregnancy status, and can therefore be used to identify animals with superior overall immune responsiveness. However, age and pregnancy status had significant effects on adaptive IR profiles, highlighting the need for appropriate statistical modeling of such effects when ranking animals on their ability to mount CM and AMIR. Responses became increasingly type 1 biased as heifers approached 12 mo of age, from which point, responses then became increasingly type 2 biased with age and length of gestation. Knowledge of how age and pregnancy influence the dynamics of type 1 and type 2 IR bias is expected to improve our ability to select animals with enhanced immune responsiveness and aid in the development of effective vaccines through strategic targeting of vaccine components to recipients.
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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 | 0.001 |
| 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) | 0.001 | 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".