{"id":"W2114137166","doi":"10.1109/tcbb.2011.28","title":"Uncovering Hidden Phylogenetic Consensus in Large Data Sets","year":2011,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; University of Ottawa","funders":"","keywords":"Phylogenetic tree; Tree (set theory); Set (abstract data type); Computer science; Heuristic; Taxon; Data mining; Data set; Biological data; Tree rearrangement; Artificial intelligence; Mathematics; Ecology; Biology; Bioinformatics; Combinatorics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.05405702,0.00160872,0.00343342,0.007846395,0.005122064,0.005211499,0.004906937,0.004300856,0.0008580605],"category_scores_gemma":[0.1570486,0.002004873,0.002581304,0.006758961,0.005734244,0.006392036,0.00657641,0.00707336,0.0006671157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00240368,"about_ca_system_score_gemma":0.00272534,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002655204,"about_ca_topic_score_gemma":0.005783755,"domain_scores_codex":[0.9697461,0.01605782,0.002096009,0.005253953,0.005988207,0.0008579874],"domain_scores_gemma":[0.798364,0.1613643,0.009975557,0.02156384,0.007001924,0.001730279],"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":[0.001158041,0.0006580228,0.08913931,0.001922226,0.001491345,0.003295115,0.01058861,0.6036731,0.02970518,0.05012789,0.008860125,0.1993811],"study_design_scores_gemma":[0.00008213037,0.0001023815,0.00715914,0.000151679,0.0001134819,0.0004758251,0.001638104,0.8430271,0.00774695,0.1356432,0.003749261,0.0001108056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3255876,0.001200324,0.6645874,0.002445439,0.0001279844,0.0002432296,0.001253247,0.003094864,0.001459908],"genre_scores_gemma":[0.4235594,0.000197576,0.5723928,0.0004856557,0.0000682939,0.000303865,0.002422088,0.0003309827,0.0002393376],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05405702,"threshold_uncertainty_score":0.2858843,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03948868239000623,"score_gpt":0.2791476389392529,"score_spread":0.2396589565492466,"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."}}