0178 High immune response technology for use in commercial swine herds: A broad based approach to disease resistance
Bibliographic record
Abstract
Societal concern regarding food safety and animal health are increasing, encompassing issues including the presence of antibiotic residues in meat, antimicrobial resistant organisms and the risk of zoonotic disease. Therefore, effective economic alternatives, with the potential to improve productivity in addition to animal health and robustness, are essential to the industry's continued success. Previous research in pigs has clearly demonstrated favorable responses to breeding pigs for high immune response (HIR). However, this method has not been tested in commercial swine herds. Since the HIR technology identifies animals with increased capacity for immune response (IR) and subsequently increased disease resistance, its implementation and integration into commercial pig breeding programs is expected to bring health and production benefits. The HIR test measures IR to benign and carefully selected test antigens (Ag), one that elicits antibody-mediated IR (AMIR) and another that elicits cell-mediated IR (CMIR). The study objective was to re-establish and refine the HIR test for pigs in a pilot study and then to utilize this test within a commercial facility. Two groups of weaned piglets, 24 piglets/group were HIR phenotyped. Antibody-mediated IR, as measured by antigen-specific ELISA, was greater in the older versus younger test piglets (p < 0.0001, un-paired t test). Cell-mediated IR, was observed by delayed-type hypersensitivity, measured by change in double-skin-fold thickness (DSFT) both 24 and 48 h after intradermal injection of CMIR-associated antigen, and did not differ between test groups. Results indicate it is possible to phenotype and rank pigs for IR using a standardized HIR protocol. Applying this protocol to approximately 3600 weaned F1-barrows from seven different swine genetics companies is now underway as part of a large collaborative Genome Canada project examining associations between IR with swine health, production and genomic information.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".