{"id":"W4320033035","doi":"10.1093/sysbio/syad006","title":"Handling Logical Character Dependency in Phylogenetic Inference: Extensive Performance Testing of Assumptions and Solutions Using Simulated and Empirical Data","year":2023,"lang":"en","type":"article","venue":"Systematic Biology","topic":"Evolution and Paleontology Studies","field":"Earth and Planetary Sciences","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of General Medical Sciences; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; Smithsonian Institution; National Science Foundation","keywords":"Inference; Character evolution; Weighting; Character (mathematics); Phylogenetic tree; Coding (social sciences); Bayesian probability; Dependency (UML); Maximum parsimony; Computer science; Character encoding; Algorithm; Artificial intelligence; Mathematics; Statistics; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0558573,0.001704189,0.001899551,0.00232904,0.001778619,0.002294028,0.003047981,0.003770324,0.00134305],"category_scores_gemma":[0.2124554,0.0009077179,0.001952937,0.002912674,0.003177533,0.005498599,0.003604336,0.00387426,0.0003114972],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002594945,"about_ca_system_score_gemma":0.002293106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004609782,"about_ca_topic_score_gemma":0.003600309,"domain_scores_codex":[0.9781833,0.01663969,0.001146211,0.002054609,0.001578191,0.0003979761],"domain_scores_gemma":[0.6001056,0.3734386,0.005997539,0.01454165,0.005002186,0.0009145513],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006241447,0.0003930686,0.02329604,0.0005784347,0.0006766822,0.0001118841,0.0005973573,0.8869131,0.001423998,0.01885855,0.000945254,0.06558145],"study_design_scores_gemma":[0.00006286877,0.0001871569,0.002046377,0.00007795753,0.00007083973,0.00006267409,0.0001076075,0.9824616,0.001318914,0.01324488,0.0003257251,0.00003330962],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6231562,0.002546415,0.3685838,0.001376792,0.00009074921,0.0002764268,0.0006435724,0.0007138918,0.002612022],"genre_scores_gemma":[0.7957714,0.000592966,0.2014074,0.0002040573,0.0000453118,0.0003852888,0.001116796,0.0002062969,0.0002704202],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0558573,"threshold_uncertainty_score":0.2954051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3460427459127692,"score_gpt":0.3736714499833783,"score_spread":0.02762870407060913,"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."}}