{"id":"W3092157588","doi":"10.1093/bioinformatics/btaa867","title":"Particle Gibbs sampling for Bayesian phylogenetic inference","year":2020,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Markov chain Monte Carlo; Gibbs sampling; Phylogenetic tree; Computer science; Particle filter; Inference; Tree (set theory); Bayesian inference; Bayesian probability; Markov chain; Sampling (signal processing); Monte Carlo method; Algorithm; Data mining; Theoretical computer science; Artificial intelligence; Mathematics; Machine learning; Statistics; Biology; Combinatorics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007454285,0.0001392678,0.0001376023,0.00001002632,0.0001053546,0.00003431378,0.000176905,0.0000723278,0.00000653814],"category_scores_gemma":[0.0001665784,0.0001316414,0.00008071723,0.00006409185,0.00004761365,0.000001243014,0.0001159185,0.00003625501,0.000016245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000469244,"about_ca_system_score_gemma":0.0000548869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000156086,"about_ca_topic_score_gemma":0.000003429634,"domain_scores_codex":[0.9992312,0.000008325456,0.0002724241,0.000155214,0.00007539607,0.0002574377],"domain_scores_gemma":[0.9995011,0.00002481664,0.00008195043,0.0001920563,0.00007273517,0.0001273517],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002282799,0.00008324831,0.02112951,0.0004301781,0.000285581,7.580618e-7,0.002944836,0.003918013,0.9095521,0.001901209,0.003438069,0.05608824],"study_design_scores_gemma":[0.003300361,0.002722441,0.0112359,0.00003476076,0.0001535933,0.00001050056,0.001764922,0.1368146,0.4206886,0.001006866,0.4208091,0.00145835],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7331757,0.001119644,0.2629805,0.0008586951,0.0001658808,0.000569435,0.0001191826,0.00001722221,0.0009937286],"genre_scores_gemma":[0.9602338,0.0001614099,0.03786518,0.001389888,0.0002271801,0.00003500879,0.00003591765,0.00001615988,0.00003544169],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4888635,"threshold_uncertainty_score":0.5368181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04092308051789186,"score_gpt":0.275039594293375,"score_spread":0.2341165137754831,"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."}}