{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004643615,0.001548349,0.002181663,0.005503193,0.002527502,0.002220109,0.004584206,0.002601271,0.003884156],"category_scores_gemma":[0.02114521,0.001390588,0.00298421,0.003868998,0.001256823,0.00377558,0.002762369,0.002662552,0.001925821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001930688,"about_ca_system_score_gemma":0.003292876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009601192,"about_ca_topic_score_gemma":0.01444496,"domain_scores_codex":[0.9963462,0.0009281755,0.0002720721,0.001227864,0.0009822269,0.0002434825],"domain_scores_gemma":[0.9906079,0.004320206,0.0009936331,0.001187844,0.002436051,0.0004544089],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000362372,0.0001891225,0.005338436,0.0005902878,0.0003826995,0.0003723853,0.001192415,0.5695617,0.007138966,0.04240996,0.01041989,0.3620417],"study_design_scores_gemma":[0.00002453729,0.00003146347,0.0004220584,0.00003406824,0.00002952477,0.00007124239,0.0001307336,0.9598744,0.001824809,0.03530078,0.002221451,0.00003488403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01133663,0.0001169679,0.986521,0.00009019904,0.00003532326,0.00008116121,0.0002525727,0.001074479,0.0004916993],"genre_scores_gemma":[0.04190245,0.00007081163,0.9559222,0.00005652664,0.00002510944,0.0001305903,0.001127062,0.0003279661,0.0004374187],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009601192,"threshold_uncertainty_score":0.02455813,"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."}}