{"id":"W2171675548","doi":"10.1093/bioinformatics/btt729","title":"Site-heterogeneous mutation-selection models within the PhyloBayes-MPI package","year":2013,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":89,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Software; Focus (optics); Scalability; Selection (genetic algorithm); Interface (matter); Model selection; Data mining; Mutation; Variety (cybernetics); Dirichlet process; Bayesian probability; Theoretical computer science; Machine learning; Artificial intelligence; Programming language; Biology; Genetics; Parallel computing; Database; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000113219,0.0001589975,0.0001068294,0.0000256606,0.0002012807,0.00008255983,0.0001711977,0.00009197734,0.00001815311],"category_scores_gemma":[0.00002864241,0.0001111211,0.00007941908,0.00008022047,0.00006852258,0.000004462193,0.00009630203,0.00006434058,0.0001152073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001269638,"about_ca_system_score_gemma":0.00004165663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005301867,"about_ca_topic_score_gemma":0.00005294196,"domain_scores_codex":[0.9992143,0.00002351198,0.0002989601,0.0001290241,0.0001253606,0.0002088636],"domain_scores_gemma":[0.9993776,0.00001397915,0.0001438171,0.0002824752,0.0001272333,0.0000548991],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001559513,0.0003622465,0.009327111,0.0003406195,0.00147485,0.000005547157,0.01461363,0.1896925,0.6695779,0.005027309,0.05943535,0.04998702],"study_design_scores_gemma":[0.001955106,0.001364427,0.009539639,0.00003487559,0.0001769509,0.0003172874,0.002967595,0.6687146,0.280434,0.01065686,0.02221413,0.001624481],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9830793,0.0004021437,0.01300891,0.0001427364,0.000199644,0.0004199421,0.00002849315,0.00001120439,0.002707686],"genre_scores_gemma":[0.9931402,0.00009790392,0.005635018,0.0005945915,0.0001361661,0.00008805542,0.00005026008,0.00001638985,0.0002414144],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4790221,"threshold_uncertainty_score":0.4531386,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01093962971221689,"score_gpt":0.2127637677099754,"score_spread":0.2018241379977585,"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."}}