{"id":"W2098875654","doi":"10.1186/1471-2156-15-53","title":"Multi-population genomic prediction using a multi-task Bayesian learning model","year":2014,"lang":"en","type":"article","venue":"BMC Genetics","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; University of Guelph; University of Alberta","funders":"Agriculture and Agri-Food Canada; Natural Sciences and Engineering Research Council of Canada; Western Canada Research Grid; Compute Canada","keywords":"Quantitative trait locus; Pooling; Computer science; Bayesian probability; Population; Multi-task learning; Machine learning; Artificial intelligence; Task (project management)","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":[0.0001738832,0.0002173852,0.0001513907,0.00005049141,0.000182435,0.00003009788,0.0001803041,0.000242508,0.000006178822],"category_scores_gemma":[0.00006985293,0.0002381916,0.00008991972,0.00005890615,0.0000516609,0.000003254817,0.0001048038,0.0001322488,0.000008662598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002521883,"about_ca_system_score_gemma":0.00006817823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002074327,"about_ca_topic_score_gemma":0.0000611309,"domain_scores_codex":[0.9986845,0.0001249935,0.0003004401,0.0004546467,0.0001315523,0.0003038756],"domain_scores_gemma":[0.9993151,0.000009519396,0.0001261981,0.0003657478,0.00006561579,0.0001177639],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001924046,0.00005086843,0.08332717,0.00001948862,0.00001548088,3.499624e-8,0.00008335451,0.6836488,0.2312573,0.00005236948,0.00001334616,0.001512503],"study_design_scores_gemma":[0.0007505933,0.0001692523,0.2530242,0.000009213201,0.00004386403,0.000006565306,0.00002937616,0.7418338,0.003276414,0.0001778827,0.0004526423,0.0002262136],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4833921,0.0002220007,0.5160476,0.000002300358,0.0001227917,0.0001324814,0.000008907752,0.00001947398,0.00005235746],"genre_scores_gemma":[0.5828897,0.0000161211,0.416411,0.00003291984,0.0002163132,0.000007770807,0.00009968661,0.00003385157,0.0002926829],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2279809,"threshold_uncertainty_score":0.9713173,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0285152328275023,"score_gpt":0.2611605218051323,"score_spread":0.23264528897763,"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."}}