{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002125498,0.0001112934,0.0001233087,0.00006476277,0.0002225189,0.00001410846,0.0001917306,0.00007666703,0.000006616126],"category_scores_gemma":[0.0001837827,0.0001096404,0.00004027216,0.0001821141,0.0007021033,0.00001136393,0.0000732435,0.00004604837,4.545813e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001016316,"about_ca_system_score_gemma":0.0001873414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005749694,"about_ca_topic_score_gemma":0.000004010539,"domain_scores_codex":[0.9990624,0.00002924248,0.0001885147,0.0003432001,0.0001231206,0.0002535142],"domain_scores_gemma":[0.9993979,0.00002984879,0.0001129333,0.0001285207,0.0002344607,0.0000962869],"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":[0.0001044184,0.0002502385,0.01563547,0.00003714533,0.00007824126,1.315642e-7,0.002556005,0.00619696,0.9552816,0.0124894,0.00006421792,0.007306169],"study_design_scores_gemma":[0.001155113,0.003121719,0.5099464,0.0000223176,0.00008153731,0.00004121667,0.0006540654,0.0178543,0.4629567,0.003558875,0.0002511244,0.0003566313],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5619187,0.0001729137,0.4374746,0.000003262021,0.00004493455,0.0002354678,0.00009953693,0.000003988453,0.00004649596],"genre_scores_gemma":[0.5085251,0.000003449509,0.4913903,0.00001043843,0.00002433093,0.00001510215,0.000005949279,0.000004942859,0.00002037034],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4943109,"threshold_uncertainty_score":0.4471005,"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."}}