{"id":"W3138529574","doi":"10.1093/bioinformatics/btac326","title":"Building alternative consensus trees and supertrees using <i>k</i> -means and Robinson and Foulds distance","year":2022,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science","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":[0.000102247,0.0001222917,0.0001248216,0.00002815839,0.0002839016,0.00003448109,0.00005743178,0.00003163294,0.0000012502],"category_scores_gemma":[0.00001985885,0.0001173728,0.00001902564,0.00003553638,0.0001391086,0.000001562303,0.0003352082,0.00005373293,7.523319e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001043502,"about_ca_system_score_gemma":0.0000186513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003427931,"about_ca_topic_score_gemma":0.00003506821,"domain_scores_codex":[0.9994504,0.00002010675,0.0001643101,0.0001409254,0.00007455517,0.0001497439],"domain_scores_gemma":[0.9997272,0.00002081758,0.00006948872,0.0001083571,0.00002291852,0.00005120136],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003088531,0.0001071288,0.08548287,0.0003334667,0.0005788078,0.00001629082,0.00864298,0.004542603,0.8519121,0.005421555,0.001600191,0.04105311],"study_design_scores_gemma":[0.008638242,0.003624997,0.05040091,0.0001399096,0.0004634386,0.001561703,0.02708886,0.4097351,0.0916679,0.003940133,0.3993437,0.003395118],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9935366,0.004301736,0.001570484,0.00007925583,0.00007739683,0.0001185584,0.000112925,0.000002970403,0.0002000519],"genre_scores_gemma":[0.9815245,0.0009409995,0.01726333,0.0001720164,0.00003928851,0.000007081249,0.000008288192,0.00001012916,0.00003437505],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7602443,"threshold_uncertainty_score":0.4786322,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01528199791329115,"score_gpt":0.2403722262908893,"score_spread":0.2250902283775981,"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."}}