{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001819546,0.0001642446,0.0001569052,0.00009155265,0.0001384903,0.00001031884,0.0002648459,0.0001468813,0.00001362486],"category_scores_gemma":[0.0000222158,0.0001528186,0.00003490063,0.00008531442,0.0001322718,0.000002484143,0.00004731696,0.0001096145,0.00001171012],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000853406,"about_ca_system_score_gemma":0.00006212171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002112593,"about_ca_topic_score_gemma":0.0001169106,"domain_scores_codex":[0.9990984,0.00004081767,0.0003181602,0.0002555624,0.00005909397,0.0002279791],"domain_scores_gemma":[0.9993837,0.00006360283,0.00007663178,0.0003712912,0.00004893353,0.00005588135],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002733302,0.003385827,0.1714171,0.0007386797,0.003379195,0.0000579146,0.01526775,0.06561356,0.07664423,0.001688065,0.001795058,0.6572793],"study_design_scores_gemma":[0.02056365,0.008061272,0.6279465,0.0003052026,0.000582984,0.0009222633,0.005597709,0.1764496,0.05424428,0.06668228,0.03300928,0.005635004],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9679726,0.0003879993,0.03002885,0.00008960223,0.0002679973,0.000188832,0.0006651168,0.000007408637,0.0003916082],"genre_scores_gemma":[0.9515941,0.0002721647,0.04754623,0.0003334668,0.00002908154,0.000009719138,0.0001881133,0.000009066944,0.00001804575],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6516442,"threshold_uncertainty_score":0.6231762,"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."}}