{"id":"W2051062503","doi":"10.1093/sysbio/syu099","title":"Bayesian Long Branch Attraction Bias and Corrections","year":2014,"lang":"en","type":"article","venue":"Systematic Biology","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Attraction; Bayesian probability; Tree (set theory); Set (abstract data type); Star (game theory); Bayesian network; Biology; Computer science; Mathematics; Statistics; Algorithm; Statistical physics; Physics; Combinatorics; Mathematical analysis","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":[0.0002280316,0.00009073263,0.0001657079,0.00003852465,0.0001034883,0.0000179042,0.00006509735,0.0001804947,0.0000221525],"category_scores_gemma":[0.0001489187,0.0000771464,0.00004108071,0.00004258247,0.00005433948,0.000001939192,0.00004935455,0.00005345989,0.00001292269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004138224,"about_ca_system_score_gemma":0.000009552821,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002193478,"about_ca_topic_score_gemma":0.00006122623,"domain_scores_codex":[0.9992467,0.000226363,0.0001766999,0.0002005238,0.00003778353,0.0001119499],"domain_scores_gemma":[0.9995777,0.00002802849,0.0001041278,0.0001974134,0.00004315659,0.00004956574],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005297696,0.00005051316,0.8592982,0.006202512,0.0002514997,8.962702e-7,0.000411223,0.0004457323,0.1250595,0.004546012,0.0004810793,0.003199811],"study_design_scores_gemma":[0.004025543,0.00201762,0.907565,0.001753062,0.0006565254,0.0008917337,0.001395346,0.008362259,0.04707744,0.01505641,0.009166669,0.0020324],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8823461,0.000219892,0.1159598,0.00008675031,0.0004957303,0.0002244838,0.000004915426,0.00001484735,0.0006475321],"genre_scores_gemma":[0.9990832,0.000009855654,0.0003453777,0.0001396899,0.0001177855,0.00001022228,0.00004887133,0.000005332571,0.0002397103],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1167371,"threshold_uncertainty_score":0.3145939,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01763570097442089,"score_gpt":0.2473932853863419,"score_spread":0.2297575844119211,"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."}}