{"id":"W4389624276","doi":"10.1186/s12711-023-00860-9","title":"Genetic analysis of the blood transcriptome of young healthy pigs to improve disease resilience","year":2023,"lang":"en","type":"article","venue":"Genetics Selection Evolution","topic":"Genetic Mapping and Diversity in Plants and Animals","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre de Développement du Porc du Québec; University of Saskatchewan; University of Alberta","funders":"U.S. Department of Agriculture; Genome Alberta; National Institute of Food and Agriculture; University of Alberta; Genome Canada; Genome Prairie","keywords":"Biology; Transcriptome; Heritability; Genetics; Gene; Genotyping; Disease; Candidate gene; Genotype; Genetic architecture; Selection (genetic algorithm); Gene expression; Quantitative trait locus; Internal medicine; Medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001454122,0.0001025843,0.0001471985,0.0002113069,0.0001257825,0.000007490878,0.0001934132,0.00007308119,0.00001071108],"category_scores_gemma":[0.00006182854,0.00009182964,0.0001649412,0.001321145,0.00005723489,0.000001797915,0.00006485278,0.00004434866,0.000003412432],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001424639,"about_ca_system_score_gemma":0.0001218041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001138143,"about_ca_topic_score_gemma":0.0001573459,"domain_scores_codex":[0.998947,0.00006696283,0.0002556682,0.0002909768,0.0002276649,0.0002117292],"domain_scores_gemma":[0.9993258,0.000007501258,0.0001186219,0.0002744272,0.0001711031,0.0001025715],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00008215097,0.00005366768,0.1860068,0.00003167639,0.0001570641,1.773858e-7,0.00007553112,0.02769046,0.7851651,0.00003549705,0.0005375064,0.0001642862],"study_design_scores_gemma":[0.0002443495,0.0003691054,0.9501143,0.000009687361,0.0005555403,0.000001408841,0.00007240046,0.004808205,0.04322882,0.00004538079,0.0004335878,0.0001171833],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.994099,0.0003076932,0.004814377,0.0001005246,0.0002346943,0.0001970838,0.0001670622,0.00001331411,0.00006623782],"genre_scores_gemma":[0.9987833,0.0002131252,0.0002891961,0.00005280194,0.00005919838,0.000009196865,0.00003473027,0.000006960884,0.0005514661],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7641075,"threshold_uncertainty_score":0.3744704,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007661981679192638,"score_gpt":0.2300765639379875,"score_spread":0.2224145822587948,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}