036 A natural challenge model for disease resilience in wean-to-finish pigs
Bibliographic record
Abstract
Selecting for tolerance or resistance to specific diseases may be detrimental for the ability of the animal to respond to other major pathogens. In commercial herds, disease often reflects the outcome of infection with multiple pathogens at different stages of development. Thus, the objective is to determine the genetic basis of resilience to multiple common diseases, which is defined as the ability to respond to infection to minimize the impact of disease. To investigate the genetic basis and develop early predictors of resilience, a natural challenge model has been established at the wean-to-finish research facilities at the Centre de Développement du Porc du Québec. In this project, batches of 60 to 76 F1 (Yorkshire × Landrace) barrows from healthy multiplier herds representing 7 PigGen Canada (www.piggencanada.org) members are entered into a clean nursery for 3 wk (at 17 to 29 d of age), where samples and phenotypes under healthy conditions are collected and then moved into a continuous flow grow-finish facility at ∼60 d of age (52 to 72 d) that was seeded using pigs from commercial farms with targeted diseases to simulate a commercial health environment. Alternating medicated and non-medicated feed is used to moderate the disease challenge within set limits. These results include the first 441 of ∼3,300 pigs that will be evaluated over 3 yr. Each pig was weighed every 3 wk, and mortality and treatment records were recorded throughout. In the finisher, individual feed and water intake were recorded. In total, 35% of pigs died or were humanely euthanized. Basic mixed models were fit with litter as a random effect as an indicator of the heritability due to the lack of pedigree and genotypes currently. Models for average daily gain (ADG) in the nursery and in the finishing unit included fixed effects of batch, entry age (nursery only), days on test (finishing only), batch*pen, and mortality (1/0) with litter as a random effect. Litter explained 25.9% of the phenotypic variation for nursery ADG and 20.5% for finishing ADG. ADG in the nursery and finishing unit showed a phenotype correlation of 0.19. Future analyses will focus on developing early predictors of resilience and include dense SNP genotype data for genome-wide association studies and genomic prediction to dissect and utilize the genetic basis of disease resilience. Funding from Genome Alberta (ALGP2), Genome Canada, and PigGen Canada.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".