{"id":"W2123271845","doi":"10.1093/bioinformatics/bts555","title":"Phylogenetics, likelihood, evolution and complexity","year":2012,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Institutes of Health; National Institute of General Medical Sciences; National Natural Science Foundation of China","keywords":"Phylogenetics; Evolutionary biology; Computational biology; Computer science; Biology; Genetics; Gene","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":[0.0001447791,0.00011908,0.00009882286,0.00002495063,0.0001031067,0.00001579483,0.00007738034,0.00007841612,0.000004602634],"category_scores_gemma":[0.00002769402,0.000109959,0.00003654468,0.00003813059,0.000119024,0.000001686745,0.0001573583,0.00003740554,0.00001999546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001034711,"about_ca_system_score_gemma":0.00001979607,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008853504,"about_ca_topic_score_gemma":0.000009699077,"domain_scores_codex":[0.9993586,0.00001347544,0.0001811815,0.00008205981,0.0000748746,0.0002898279],"domain_scores_gemma":[0.999577,0.000005568676,0.00006478099,0.0001936049,0.00004382508,0.0001152192],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000103588,0.0003208621,0.6267393,0.0003017092,0.0004084752,4.456171e-7,0.002635377,0.00004381482,0.2931276,0.0118327,0.02155433,0.04293176],"study_design_scores_gemma":[0.001165985,0.0004722093,0.7321664,0.00001614792,0.00008615274,0.00006801252,0.001283389,0.001543728,0.03472638,0.001751468,0.2258911,0.0008289575],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.987091,0.004251773,0.002970719,0.00004127099,0.0001631917,0.0001403021,0.0000193527,0.000005596169,0.005316796],"genre_scores_gemma":[0.9868879,0.0003823396,0.01217619,0.0001781606,0.0002641255,0.000006195765,0.00002583439,0.000009728707,0.00006950211],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2584013,"threshold_uncertainty_score":0.4483997,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02227399503939966,"score_gpt":0.2369459821068522,"score_spread":0.2146719870674526,"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."}}