{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004984586,0.00114289,0.002006724,0.0009113204,0.0006630247,0.001614752,0.002910127,0.002283412,0.001897306],"category_scores_gemma":[0.008039742,0.0008737585,0.001628364,0.001053426,0.00114849,0.002130311,0.001604305,0.002430992,0.0004841557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001713999,"about_ca_system_score_gemma":0.001578859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0196845,"about_ca_topic_score_gemma":0.0112382,"domain_scores_codex":[0.9983532,0.0006479904,0.00008445822,0.0005147501,0.0002234592,0.0001760638],"domain_scores_gemma":[0.9952317,0.003313396,0.0003958246,0.0001799768,0.0007189563,0.0001601915],"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":[0.00009126585,0.00005929738,0.001846115,0.00003707778,0.0000768468,0.00006840478,0.00008779772,0.9724025,0.0004426755,0.004770475,0.000383652,0.01973385],"study_design_scores_gemma":[0.000007932771,0.00001333288,0.0001859471,0.000003694954,0.00001070102,0.000007373572,0.000003683853,0.9969478,0.00006502524,0.002671949,0.00007661685,0.000005986462],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05073202,0.0003651414,0.9462478,0.0007194256,0.0000386318,0.00008155422,0.000208321,0.0002873453,0.00131974],"genre_scores_gemma":[0.8474715,0.0004298786,0.1451464,0.0005806843,0.000125831,0.0005221097,0.0006802189,0.00007719325,0.004966177],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0196845,"threshold_uncertainty_score":0.03913981,"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."}}