{"id":"W3018643613","doi":"10.1016/j.mex.2020.100892","title":"Optimization of Genotype by Sequencing data for phylogenetic purposes","year":2020,"lang":"en","type":"article","venue":"MethodsX","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Ontario Museum; University of Toronto","funders":"National Museum of Natural History; Royal Ontario Museum; Universidade Estadual Paulista; Universidade Federal de Mato Grosso do Sul; Instituto Politécnico Nacional; Universidade Federal de Lavras; Universidade Federal de Minas Gerais; Academy of Natural Sciences of Drexel University; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Pontifícia Universidade Católica de Minas Gerais; Conservation International; American Museum of Natural History","keywords":"Genotype; Phylogenetic tree; Biology; DNA sequencing; Computational biology; Biotechnology; Genetics; Gene","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":[0.0002120681,0.00009867588,0.000153266,0.000009955702,0.00004378797,0.000006738569,0.0002996196,0.00006243744,0.000005586033],"category_scores_gemma":[0.0003090151,0.00009648316,0.00004043895,0.00005695248,0.00005161012,6.127746e-7,0.000217589,0.00002340068,5.135075e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003997255,"about_ca_system_score_gemma":0.00004884028,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009553993,"about_ca_topic_score_gemma":0.000001592605,"domain_scores_codex":[0.9992567,0.00006163155,0.000177411,0.0003212722,0.00005392838,0.0001290663],"domain_scores_gemma":[0.9993929,0.00003180001,0.00008588985,0.0003648702,0.000076735,0.0000478048],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004438054,0.000008348719,0.0002430967,0.0000359054,0.00006733149,9.55505e-8,0.00007484115,0.008179575,0.9837838,0.00001384593,0.001861228,0.005687492],"study_design_scores_gemma":[0.0005319715,0.000493767,0.0001522752,0.000004529303,0.00008687197,0.000001572965,0.0001071481,0.01863024,0.8822286,0.00013354,0.09739708,0.0002324736],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1314277,0.007140617,0.8599231,0.0002933533,0.0001003292,0.000348286,0.0005648247,0.000004058267,0.0001977917],"genre_scores_gemma":[0.3586037,0.0005259492,0.6397641,0.0004340498,0.0001910132,0.00001824374,0.0003877781,0.0000249912,0.0000502499],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.227176,"threshold_uncertainty_score":0.3934469,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08939029299588051,"score_gpt":0.3254345456192921,"score_spread":0.2360442526234116,"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."}}