{"id":"W2098878547","doi":"10.1093/molbev/msp248","title":"A Dirichlet Process Covarion Mixture Model and Its Assessments Using Posterior Predictive Discrepancy Tests","year":2009,"lang":"en","type":"article","venue":"Molecular Biology and Evolution","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Génome Québec; Genome Canada","keywords":"Dirichlet process; Dirichlet distribution; Biology; Probabilistic logic; Phylogenetic tree; Sequence (biology); Biological system; Bayesian probability; Statistical physics; Mathematics; Evolutionary biology; Statistics; Genetics; Physics","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":[0.03924983,0.001326773,0.003014072,0.005635893,0.001726436,0.003728495,0.004891135,0.004364731,0.004192743],"category_scores_gemma":[0.1970066,0.001204164,0.002162931,0.003569605,0.005004314,0.006484459,0.004607943,0.004737821,0.0005899731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002185445,"about_ca_system_score_gemma":0.001955063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00429253,"about_ca_topic_score_gemma":0.002044391,"domain_scores_codex":[0.9834083,0.01215008,0.0006072725,0.001757098,0.001650743,0.0004264212],"domain_scores_gemma":[0.7557488,0.2258321,0.006089755,0.006580765,0.00411371,0.001634805],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0008511943,0.0001525581,0.02048524,0.0002723862,0.0003848983,0.0003978445,0.0008551519,0.579837,0.001015371,0.3272306,0.002708841,0.06580889],"study_design_scores_gemma":[0.00003875062,0.00003374485,0.00108986,0.00003558305,0.00002643871,0.00009562869,0.00005731101,0.8687899,0.0003277906,0.1289564,0.000506814,0.00004178823],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09578227,0.0004285137,0.9002736,0.0008070788,0.00004078424,0.0001148075,0.0002570855,0.0003939571,0.001901916],"genre_scores_gemma":[0.7531024,0.000382524,0.2429147,0.0002453976,0.0001814292,0.0004500023,0.001188992,0.0002438442,0.001290707],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03924983,"threshold_uncertainty_score":0.2075754,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01423513034969218,"score_gpt":0.338292050934288,"score_spread":0.3240569205845958,"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."}}