{"id":"W1982013891","doi":"10.1016/j.livsci.2011.06.010","title":"Optimal selection for multiple quantitative trait loci and contributions of individuals using genetic algorithm","year":2011,"lang":"en","type":"article","venue":"Livestock Science","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Sichuan University; Sichuan Agricultural University; National Natural Science Foundation of China","keywords":"Selection (genetic algorithm); Quantitative trait locus; Trait; Population; Mathematics; Inbreeding; Genetic algorithm; Statistics; Mathematical optimization; Biology; Computer science; Genetics; Gene; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"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":[0.004891253,0.001169602,0.00211547,0.001553002,0.0008161717,0.000900932,0.001950462,0.001581591,0.001977646],"category_scores_gemma":[0.00930097,0.0008168684,0.001312431,0.0009763179,0.001131732,0.001385473,0.001134833,0.001067459,0.0002179447],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008463838,"about_ca_system_score_gemma":0.001488138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00301767,"about_ca_topic_score_gemma":0.003114302,"domain_scores_codex":[0.9985057,0.0008606724,0.00005253571,0.0003183632,0.0001350045,0.0001278606],"domain_scores_gemma":[0.9940247,0.004967086,0.0001595964,0.0002094563,0.0004871968,0.0001519749],"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":[0.0003804833,0.0002119366,0.005585055,0.00003870474,0.0002440255,0.0001048739,0.0001393662,0.921812,0.003017595,0.01086238,0.0005697472,0.05703386],"study_design_scores_gemma":[0.00004898565,0.00002279507,0.0003520679,0.000003358437,0.00002559677,0.00001615802,0.000007412611,0.9958759,0.0001953707,0.003381785,0.0000656263,0.000005009008],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1646457,0.0001714615,0.833585,0.0003128268,0.00004675305,0.00006146199,0.0000400008,0.0002795483,0.0008571303],"genre_scores_gemma":[0.6004804,0.0001029147,0.395923,0.0001885512,0.00009732675,0.0002372007,0.0001557071,0.0001028421,0.002712079],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004891253,"threshold_uncertainty_score":0.0258677,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03224774705895707,"score_gpt":0.2911915494461179,"score_spread":0.2589438023871609,"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."}}