{"id":"W2936057968","doi":"10.3934/genet.2018.4.212","title":"Starless bias and parameter-estimation bias in the likelihood-based phylogenetic method","year":2018,"lang":"en","type":"article","venue":"AIMS Genetics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Tree (set theory); Phylogenetic tree; Star (game theory); Star network; Equidistant; Mathematics; Maximum likelihood; Statistics; Network topology; Biology; Algorithm; Topology (electrical circuits); Astrophysics; Computer science; Combinatorics; Physics; Genetics; Geometry","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":[0.06817497,0.001462109,0.002020158,0.003766774,0.002021277,0.005652581,0.00363336,0.003150671,0.002487405],"category_scores_gemma":[0.2829039,0.0012554,0.002169213,0.004336397,0.006712785,0.007292187,0.005037158,0.005532275,0.001375203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00218951,"about_ca_system_score_gemma":0.001524387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001118726,"about_ca_topic_score_gemma":0.001484057,"domain_scores_codex":[0.937105,0.04656066,0.00348528,0.005846433,0.00608856,0.000914049],"domain_scores_gemma":[0.7423637,0.218017,0.009429251,0.02141179,0.007663785,0.001114531],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001069386,0.0001722516,0.1184656,0.001371979,0.001236647,0.001483579,0.007932326,0.09002129,0.01602523,0.4294843,0.00376237,0.3289752],"study_design_scores_gemma":[0.0001122501,0.0002008085,0.01181063,0.0003857585,0.0002151795,0.001770044,0.0009794894,0.4636219,0.01690007,0.4936608,0.01002977,0.0003133262],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03763875,0.000611254,0.9583588,0.0008361097,0.0001076187,0.0000949209,0.00009558174,0.0004681314,0.001788939],"genre_scores_gemma":[0.3573989,0.0003950139,0.6390767,0.000888127,0.0001606724,0.0002614178,0.0002429243,0.0007394424,0.0008369113],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.06817497,"threshold_uncertainty_score":0.360548,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04640775434520349,"score_gpt":0.304254844540514,"score_spread":0.2578470901953105,"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."}}