{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007784299,0.001167604,0.0009555902,0.001491137,0.001053089,0.002048521,0.000873277,0.0008319835,0.006920827],"category_scores_gemma":[0.0225703,0.0005489785,0.001450109,0.002695543,0.0005240042,0.001519434,0.0009349171,0.00142119,0.004237175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007168818,"about_ca_system_score_gemma":0.001618846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002286322,"about_ca_topic_score_gemma":0.006528856,"domain_scores_codex":[0.9938491,0.003320907,0.0004287316,0.001339529,0.0008803397,0.0001813208],"domain_scores_gemma":[0.9885604,0.006127769,0.0008878369,0.0032573,0.001038869,0.0001277877],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001187033,0.0004075474,0.0577834,0.0009443223,0.000914594,0.0004854695,0.0009289472,0.06299665,0.2411784,0.01364354,0.007342668,0.6121874],"study_design_scores_gemma":[0.0001947258,0.0006687481,0.1176822,0.000227074,0.0005192083,0.001442728,0.0006744153,0.5232624,0.2500741,0.02956274,0.07546676,0.0002250031],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1310505,0.0003465879,0.852909,0.0003021286,0.0001452694,0.000317113,0.00443186,0.00746854,0.003029015],"genre_scores_gemma":[0.1986941,0.0001752598,0.7874023,0.0002641485,0.00003974985,0.0005403781,0.007508562,0.003246292,0.002129211],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007784299,"threshold_uncertainty_score":0.04116774,"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."}}