{"id":"W4255949132","doi":"10.1177/117693430600200005","title":"A New Effective Method for Estimating Missing Values in the Sequence Data Prior to Phylogenetic Analysis","year":2006,"lang":"en","type":"article","venue":"Evolutionary Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"","keywords":"Missing data; Phylogenetic tree; Inference; Computer science; Probabilistic logic; Representation (politics); Sequence (biology); Data mining; Artificial intelligence; Biology; Machine learning; Genetics","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":[0.01258427,0.001462324,0.002084243,0.004211534,0.001389542,0.002354774,0.004406905,0.002624783,0.003017033],"category_scores_gemma":[0.04310971,0.001242338,0.002198369,0.003646974,0.001412682,0.004063811,0.002737193,0.004431027,0.001605603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007033424,"about_ca_system_score_gemma":0.00289142,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009964576,"about_ca_topic_score_gemma":0.001229392,"domain_scores_codex":[0.9902535,0.004923044,0.0005812664,0.001516011,0.002496078,0.00023002],"domain_scores_gemma":[0.9784273,0.0143834,0.00134919,0.00314132,0.002329954,0.0003688674],"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":[0.0004080382,0.0002016407,0.004738335,0.001180317,0.0007732903,0.0005420767,0.0006459831,0.08974285,0.02342397,0.08796764,0.005435884,0.7849401],"study_design_scores_gemma":[0.000202347,0.0003483518,0.002554093,0.0002433421,0.0003602171,0.001494068,0.0001428649,0.8011206,0.01672785,0.1473612,0.02919342,0.0002515152],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004946944,0.0000784742,0.9991469,0.00003320431,0.00003007743,0.00001522584,0.00003624313,0.0001178501,0.00004724471],"genre_scores_gemma":[0.01255617,0.0002100441,0.9861485,0.0000844009,0.0001284313,0.0001895351,0.0003136293,0.00008863479,0.000280618],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01258427,"threshold_uncertainty_score":0.06655276,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02410080544604845,"score_gpt":0.3173990370722969,"score_spread":0.2932982316262484,"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."}}