{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005327513,0.00009297713,0.0001041826,0.00006783351,0.0002627608,0.0001696039,0.0001921135,0.00004764114,3.518422e-7],"category_scores_gemma":[0.0001826496,0.00008600365,0.00002182905,0.0001577616,0.00004340055,0.001070006,0.0001814097,0.00006814916,0.000002184765],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003663236,"about_ca_system_score_gemma":0.00003403573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001471178,"about_ca_topic_score_gemma":0.000003006139,"domain_scores_codex":[0.9993294,0.00001970894,0.0001932183,0.0001307471,0.000118745,0.0002082193],"domain_scores_gemma":[0.9993659,0.000114211,0.00008874373,0.0001860109,0.0001772338,0.00006789698],"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":[0.000008625115,0.00001119026,0.0002519756,0.00008463855,0.00001342693,3.954743e-7,0.007406994,0.000004274257,0.0008420484,0.1199996,0.0003503487,0.8710265],"study_design_scores_gemma":[0.0002405106,0.0002912737,0.001338818,0.00007752763,0.000009400335,0.00003057613,0.00004202332,0.9071298,0.009241336,0.0804938,0.0009122177,0.0001927148],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004552093,0.00001788411,0.9924436,0.0001866364,0.0002434345,0.0001809185,0.000001653791,0.000121499,0.00225225],"genre_scores_gemma":[0.02718266,0.000002545116,0.972388,0.000151326,0.0001940079,0.0000198419,6.718806e-7,0.00000606425,0.00005492739],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9071255,"threshold_uncertainty_score":0.3507127,"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."}}