{"id":"W2904816621","doi":"10.1093/bioinformatics/bty1019","title":"ModL: exploring and restoring regularity when testing for positive selection","year":2018,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Type I and type II errors; Selection (genetic algorithm); Context (archaeology); Statistics; Statistical power; Null hypothesis; Statistical hypothesis testing; Mathematics; Mixing (physics); Likelihood-ratio test; Power (physics); Model selection; Type (biology); Variety (cybernetics); Chi-square test; Computer science; Artificial intelligence; Biology","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":[0.03543792,0.00202596,0.001924871,0.003351462,0.001408136,0.003655623,0.007167456,0.002696879,0.01279266],"category_scores_gemma":[0.1864943,0.001819095,0.002254598,0.003306872,0.0047664,0.005518235,0.005999514,0.004176021,0.004677554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001451242,"about_ca_system_score_gemma":0.004141608,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001833736,"about_ca_topic_score_gemma":0.002551584,"domain_scores_codex":[0.9811859,0.01166293,0.001314382,0.002692128,0.002622144,0.0005224533],"domain_scores_gemma":[0.8520886,0.1268611,0.005549079,0.01087634,0.003325285,0.001299562],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002519803,0.000413743,0.07749614,0.003006368,0.0009657564,0.002080384,0.001407775,0.1013106,0.01777316,0.1030242,0.1246844,0.5653177],"study_design_scores_gemma":[0.0007676629,0.0003652296,0.005767873,0.0003860286,0.0002260632,0.001692444,0.0002174103,0.6872228,0.0193074,0.2511315,0.03267365,0.0002420007],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01464384,0.0003842359,0.9498072,0.001594983,0.0001856961,0.0003505957,0.002416115,0.02857408,0.002043228],"genre_scores_gemma":[0.1004244,0.0002070143,0.8860695,0.001121989,0.0002707907,0.001071154,0.003402287,0.006360627,0.001072242],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03543792,"threshold_uncertainty_score":0.1874159,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09692745390537694,"score_gpt":0.2875576846226645,"score_spread":0.1906302307172876,"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."}}