{"id":"W178313090","doi":"10.1090/dimacs/061/14","title":"How good can a consensus get? Assessing the reliability of consensus trees in phylogenetic studies","year":2003,"lang":"en","type":"book-chapter","venue":"DIMACS series in discrete mathematics and theoretical computer science","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Phylogenetic tree; Reliability (semiconductor); Computer science; Biology; Genetics; Physics; Gene","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":[0.07206026,0.001237113,0.003109138,0.009774715,0.003480337,0.007087406,0.004414887,0.007941742,0.002198511],"category_scores_gemma":[0.5455394,0.001478591,0.002349913,0.01086254,0.00696162,0.01816137,0.005248572,0.005686683,0.0008775573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001775971,"about_ca_system_score_gemma":0.001524341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002065729,"about_ca_topic_score_gemma":0.002937727,"domain_scores_codex":[0.9245274,0.04876011,0.005235866,0.009884989,0.01038993,0.001201658],"domain_scores_gemma":[0.3922826,0.5272963,0.02041339,0.03325989,0.02258579,0.004162123],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001681104,0.0002904635,0.3167259,0.003020161,0.008440174,0.001665054,0.01543507,0.1949884,0.004707346,0.08694792,0.01576903,0.3503295],"study_design_scores_gemma":[0.0001478101,0.000353695,0.04204838,0.000667638,0.00128155,0.001078187,0.004189245,0.2507901,0.003043693,0.6912786,0.004846911,0.0002741765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4262117,0.009396678,0.5469887,0.006878108,0.0005605437,0.000191212,0.001607683,0.001021199,0.007144333],"genre_scores_gemma":[0.9064736,0.0008875971,0.09000171,0.0004154028,0.0002400863,0.0001361954,0.001253451,0.0003182147,0.0002738213],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.07206026,"threshold_uncertainty_score":0.3810956,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02023087159474874,"score_gpt":0.2870178750129633,"score_spread":0.2667870034182145,"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."}}