{"id":"W4409216400","doi":"10.1093/molbev/msag117","title":"IQ-TREE 3: Phylogenomic Inference Software using Complex Evolutionary Models","year":2025,"lang":"en","type":"preprint","venue":"Molecular Biology and Evolution","topic":"Evolution and Genetic Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":331,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Centre for Innovation in Biomedical Imaging Technology, Australian Research Council; National Cancer Institute; Biotechnology and Biological Sciences Research Council; Natural Sciences and Engineering Research Council of Canada; Medical Research Council; Chan Zuckerberg Initiative; National Institute for Health and Care Research; Centre of Excellence for Quantum Computation and Communication Technology, Australian Research Council; National Computational Infrastructure; EMBL Australia; National Foundation for Science and Technology Development; European Molecular Biology Laboratory; Australian Government; Gordon and Betty Moore Foundation; National Science Foundation","keywords":"Inference; Computer science; Tree (set theory); Software evolution; Phylogenomics; Software; Machine learning; Artificial intelligence; Phylogenetics; Biology; Programming language; Software development; Mathematics; Clade; Genetics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001984454,0.0004780659,0.0004105829,0.000202537,0.0002493425,0.00002726625,0.0003880967,0.001234377,0.00001151778],"category_scores_gemma":[0.0001208252,0.0005368806,0.0002159201,0.0001202231,0.0004238719,0.000005009175,0.001447645,0.0004252674,0.000004990395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001446759,"about_ca_system_score_gemma":0.0005536042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001821559,"about_ca_topic_score_gemma":0.00009955505,"domain_scores_codex":[0.9976367,0.000287135,0.0004399928,0.00105119,0.0001149199,0.0004700026],"domain_scores_gemma":[0.9986842,0.00001956488,0.0002202997,0.0007162295,0.000233317,0.0001263851],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002596291,0.0002058168,0.02276668,0.0003459423,0.0006047511,0.000009303565,0.0000593761,0.2522865,0.696179,0.02332754,0.001360371,0.002595107],"study_design_scores_gemma":[0.002036264,0.0005760555,0.0578119,0.0002991787,0.000537858,0.0001144088,0.00008793776,0.6794331,0.003138821,0.2496948,0.003793129,0.002476568],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.299969,0.008262509,0.6898497,0.000101572,0.0003950895,0.0003761317,0.0004100124,0.00004812579,0.0005878476],"genre_scores_gemma":[0.9624335,0.0006387995,0.03301394,0.0003817837,0.0001645347,0.00004835286,0.002974818,0.00002902201,0.0003152868],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6930402,"threshold_uncertainty_score":0.9997083,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0224946551662297,"score_gpt":0.3060605985958865,"score_spread":0.2835659434296569,"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."}}