{"id":"W2096066492","doi":"10.1093/bioinformatics/btu485","title":"Monte Carlo algorithms for Brownian phylogenetic models","year":2014,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Agence Nationale de la Recherche","keywords":"Monte Carlo method; Markov chain Monte Carlo; Discretization; Algorithm; Statistical physics; Computer science; Brownian motion; Hybrid Monte Carlo; Mathematics; Mathematical optimization; Physics; Statistics; Mathematical analysis","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004998026,0.001512992,0.001936948,0.001985204,0.001226593,0.002592518,0.003425959,0.002893429,0.01066256],"category_scores_gemma":[0.02544498,0.001232314,0.001817062,0.002364227,0.002126605,0.003531992,0.003020349,0.00417426,0.003098406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002098161,"about_ca_system_score_gemma":0.002607919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006005798,"about_ca_topic_score_gemma":0.00562948,"domain_scores_codex":[0.9973705,0.00141704,0.0001546081,0.0003533775,0.0005329331,0.0001715993],"domain_scores_gemma":[0.9855208,0.01186054,0.000553598,0.0007206143,0.001003857,0.0003406097],"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":[0.00005915366,0.00005115262,0.0007767549,0.0001740615,0.00006416067,0.00007805052,0.0001195398,0.6839139,0.0003770983,0.2719756,0.003301169,0.0391094],"study_design_scores_gemma":[0.00002047246,0.000007011771,0.00004339682,0.00002734527,0.000007313233,0.00002384035,0.000006856349,0.8609198,0.0001287204,0.1365505,0.00225387,0.00001097025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001008097,0.0003029653,0.9969018,0.0001270383,0.00003437362,0.00005487266,0.00008399974,0.0002942318,0.001192616],"genre_scores_gemma":[0.0649019,0.0009982744,0.927012,0.0002803039,0.0002088207,0.0008577493,0.0008876051,0.0005807178,0.004272628],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01066256,"threshold_uncertainty_score":0.0356698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02155103202997616,"score_gpt":0.2363251618297108,"score_spread":0.2147741297997346,"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."}}