{"id":"W2164555431","doi":"10.1093/bioinformatics/btm532","title":"Uniformization for sampling realizations of Markov processes: applications to Bayesian implementations of codon substitution models","year":2007,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":70,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Canadian Institute for Advanced Research","funders":"Canadian Institutes of Health Research; Centre National de la Recherche Scientifique; Canadian Institute for Advanced Research","keywords":"Computer science; Markov chain Monte Carlo; Uniformization (probability theory); Substitution (logic); Tree (set theory); Bayesian probability; Algorithm; Probabilistic logic; Theoretical computer science; Pruning; Range (aeronautics); Markov chain; Markov model; Mathematics; Artificial intelligence; Variable-order Markov model; Machine learning; Combinatorics; Programming language","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":[0.01010664,0.0006790762,0.0009668666,0.001124077,0.0008134867,0.001360623,0.00236042,0.001410875,0.003861758],"category_scores_gemma":[0.04273402,0.0008693231,0.0008588953,0.001460583,0.001766001,0.002676586,0.002526903,0.002318594,0.0007802269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001388683,"about_ca_system_score_gemma":0.001654625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003738157,"about_ca_topic_score_gemma":0.004278549,"domain_scores_codex":[0.9967892,0.002174036,0.0001630591,0.0003133042,0.0004219714,0.0001385407],"domain_scores_gemma":[0.9826188,0.01396536,0.0007087942,0.0014872,0.0008890453,0.0003307593],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002494801,0.0001259486,0.003354899,0.0001353411,0.00007368509,0.0001553734,0.0004583026,0.5220842,0.002883429,0.3565651,0.001538329,0.1123759],"study_design_scores_gemma":[0.00003015044,0.0000158536,0.0001463986,0.00001727125,0.000005680601,0.00003119644,0.00001024475,0.9438704,0.0006653306,0.05462309,0.0005728784,0.00001137302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004841287,0.00006451114,0.9944835,0.00005549065,0.000006667492,0.0000333027,0.00002071564,0.0001968914,0.0002975595],"genre_scores_gemma":[0.2077921,0.0002667347,0.7899575,0.0001153105,0.00006286801,0.0004764595,0.0002905693,0.0002375206,0.0008009356],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01010664,"threshold_uncertainty_score":0.05344963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03332797274649427,"score_gpt":0.3133691414195997,"score_spread":0.2800411686731054,"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."}}