{"id":"W2805555771","doi":"10.1186/s12862-018-1163-8","title":"A new fast method for inferring multiple consensus trees using k-medoids","year":2018,"lang":"en","type":"article","venue":"BMC Evolutionary Biology","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Phylogenetic tree; Concatenation (mathematics); Phylogenetic network; Tree rearrangement; Tree (set theory); Biology; Cluster analysis; Phylogenetics; Horizontal gene transfer; Set (abstract data type); Phylogenomics; Evolutionary biology; Computational biology; Gene; Computer science; Artificial intelligence; Genetics; Mathematics; Combinatorics; Clade","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":[0.004207029,0.001903277,0.002324457,0.004912706,0.001870124,0.001796412,0.004557831,0.003016455,0.004842403],"category_scores_gemma":[0.01418227,0.001368644,0.003433353,0.003175463,0.001272851,0.002933041,0.00279605,0.00378583,0.002491154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001591573,"about_ca_system_score_gemma":0.002979756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006040716,"about_ca_topic_score_gemma":0.007270634,"domain_scores_codex":[0.9960084,0.0009805837,0.0003736629,0.00130278,0.001110287,0.0002242502],"domain_scores_gemma":[0.9908158,0.005040431,0.0008227874,0.0008155253,0.002181539,0.000323915],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005222084,0.00013086,0.003395642,0.0005776364,0.0003678809,0.0002325045,0.000594872,0.3738174,0.01117777,0.01607048,0.007913985,0.5851987],"study_design_scores_gemma":[0.00004710757,0.00004748192,0.0003537902,0.00004588971,0.00003591967,0.0001540905,0.00009361991,0.9752719,0.003108757,0.01736941,0.003433046,0.00003914415],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002179173,0.00010663,0.9965701,0.00005134376,0.00002578618,0.00005707079,0.0001430625,0.0007033048,0.0001635194],"genre_scores_gemma":[0.02671461,0.00007552465,0.971664,0.00006222065,0.00003366137,0.0001715552,0.0006996663,0.0002010631,0.0003778375],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006040716,"threshold_uncertainty_score":0.02224916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04101743568980019,"score_gpt":0.321671346539846,"score_spread":0.2806539108500458,"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."}}