{"id":"W2744971808","doi":"10.1093/sysbio/syx068","title":"Modeling Site Heterogeneity with Posterior Mean Site Frequency Profiles Accelerates Accurate Phylogenomic Estimation","year":2017,"lang":"en","type":"article","venue":"Systematic Biology","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":545,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Austrian Science Fund","keywords":"PMSF; Tree (set theory); Mixture model; Speedup; Biological system; Computer science; Mathematics; Biology; Statistics; Parallel computing; 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":[0.0003554756,0.0003269189,0.0005299348,0.00004853871,0.0004994744,0.0001518445,0.0004471402,0.0002061575,0.000004049405],"category_scores_gemma":[0.00010937,0.0002386649,0.00010708,0.00002580122,0.0001627064,0.000004763644,0.0003420321,0.00008238603,0.00003030992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002188237,"about_ca_system_score_gemma":0.00006594689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001719872,"about_ca_topic_score_gemma":0.000479657,"domain_scores_codex":[0.9983178,0.0001680713,0.0004987938,0.0005676331,0.00007697222,0.000370752],"domain_scores_gemma":[0.9982507,0.00001767702,0.0004366126,0.001036885,0.0001774304,0.00008070665],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005482915,0.00002562247,0.04257235,0.001716944,0.0002644533,0.000003056816,0.0002706144,0.002393522,0.9525114,0.00009951297,0.000001973873,0.00008570373],"study_design_scores_gemma":[0.005756094,0.004304203,0.08836512,0.003953882,0.001117392,0.0006564845,0.0009770235,0.2429264,0.6435521,0.004317319,0.00002171766,0.004052182],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9921668,0.001523756,0.004856115,0.00008959364,0.0001914921,0.0009612587,0.00009237895,0.00001211189,0.0001065278],"genre_scores_gemma":[0.9954447,0.00005180547,0.003940221,0.00007678654,0.0001336552,0.0001956923,0.0001044955,0.00003294949,0.00001968782],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3089593,"threshold_uncertainty_score":0.9732472,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02829773744617561,"score_gpt":0.2798495343751516,"score_spread":0.2515517969289761,"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."}}