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Record W4319734232 · doi:10.3920/978-90-8686-940-4_797

797. Genetic relationships among immune response traits of young healthy pigs evaluated by immunoassays

2022· article· en· W4319734232 on OpenAlexaff
V. Bhatia, Julie Schmied, Jian Cheng, Xue Bai, Bonnie A. Mallard, Frédéric Fortin, John C. S. Harding, Michael K. Dyck, Graham Plastow, Catherine J. Field, Claire Rogel Gaillard, Fany Blanc, PigGen Canada, Jack C. M. Dekkers

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEffects of Environmental Stressors on Livestock
Canadian institutionsCentre de Développement du Porc du QuébecUniversity of SaskatchewanUniversity of AlbertaUniversity of Guelph
Fundersnot available
KeywordsImmune systemBiologyImmunologyPhenotypeAntibodyGeneticsGene

Abstract

fetched live from OpenAlex

This study estimates phenotypic and genetic correlations of different components of the immune system of young healthy pigs, evaluated by various immunoassays, with an aim towards revealing a deeper understanding of the immune system of pigs and the role of immunoassays in predicting disease resilience. The composition of circulating immune cells, their phagocytic activity, and immune response measured by the High Immune Response (HIR™) technology were found to be heritable. The percentages of granulocytes and neutrophils in blood had high positive phenotypic and genetic correlations with each other and with the percentage of cells that showed phagocytosis activity. The HIR tests showed antibody-mediated immune response to be positively genetically correlated with cell composition traits, while cell-mediated immune response had a positive genetic correlation with only % eosinophils. These results suggest that immunoassays conducted on young healthy pigs provide important genetic information on their immune response profiles.

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 score0.001
Threshold uncertainty score0.004

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 head0.021
GPT teacher head0.225
Teacher spread0.204 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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Same topicEffects of Environmental Stressors on LivestockFrench-language works237,207