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Record W4414831158 · doi:10.1093/jas/skaf300.461

PSV-10 Genetic parameters of disease in growing pigs under a polymicrobial natural disease challenge.

2025· article· en· W4414831158 on OpenAlexaffabout
Usamah Kabuye, John C. S. Harding, Michael K. Dyck, Frédéric Fortin, Graham Plastow, Tom Rathje, Jack C. M. Dekkers

Bibliographic record

VenueJournal of Animal Science · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsCentre de Développement du Porc du QuébecUniversity of AlbertaUniversity of Saskatchewan
FundersNational Institute of Food and AgricultureU.S. Department of Agriculture
KeywordsHeritabilityDiseaseGenetic correlationLitterAnimal modelOutbreakFlockGenetic variation

Abstract

fetched live from OpenAlex

Abstract This study aimed to estimate genetic parameters of disease-related traits of growing pigs exposed to a natural polymicrobial disease challenge. The challenge was established by introducing seeder pigs into a wean to finish research facility and maintained by introducing a new batch of 60 or 75, ~40 day old clinically healthy Yorkshire x Landrace barrows into the challenge nursery (cNur) every 3 weeks, with a 1-week overlap with the previous batch, before the latter was moved to the finisher (FIN). Data used included individual health treatment(s) and mortality records on 4095 barrows from 7 breeding companies. Traits analyzed included treatment rates (TRR) and mortalities (MOR), categorized as meningitis (ME), respiratory distress (RD), scours (SC), unthrifty (UNTH), and other infections (OT). Analyses were by generalized and linear mixed models with genomic relationships. Heritability estimates for TRR and MOR were generally higher in cNur than in FIN. Compared to other disease categories, RD tended to have higher heritability estimates both in cNur (TRR-RD: 0.14±0.03, MOR-RD: 0.09±0.08) and FIN (TRR-RD: 0.07±0.03, MOR-RD: 0.18±0.09). Litter effects were generally low both in cNur and Fin but stronger in cNur (TRR-RD: 0.04±0.02, MOR-UNTH: 0.05±0.02). For RD, genetic correlation estimates among TRR and MOR in cNur and FIN were generally positive ranging from 0.40±0.18 for cNur TRR with FIN MOR to 0.95±0.30 for cNur MOR with FIN MOR. Corresponding genetic correlation estimates for other disease categories were either moderately negative (-0.19±0.32 for OT TRR between cNur and FIN), not positive definite, or the model did not converge. This study considered both TRR and MOR for different disease categories and growth phases for a large number of pigs under a severe natural polymicrobial disease challenge and provides valuable information to breed for disease resilience. Funding from Genome Canada, Genome Alberta, Genome Prairie, PigGen Canada, USDA NIFA (2017-67007-26144) and the National Pork Board Survivability Project.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.009
GPT teacher head0.261
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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