{"id":"W2140183618","doi":"10.2202/1544-6115.1569","title":"Information Metrics in Genetic Epidemiology","year":2011,"lang":"en","type":"article","venue":"Statistical Applications in Genetics and Molecular Biology","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Multiple Sclerosis Society; National Multiple Sclerosis Society; Mitacs; U.S. Department of Defense; National Institutes of Health; National Science Foundation","keywords":"Computer science; Probabilistic logic; Context (archaeology); Relation (database); Interpretation (philosophy); Contrast (vision); Multinomial distribution; Genetic epidemiology; Data mining; Data science; Machine learning; Artificial intelligence; Econometrics; Mathematics; Biology; Genetics; Gene","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002453596,0.0001424268,0.0001813692,0.0002392103,0.00003351562,0.00000597599,0.0001685688,0.0002240802,0.00002037765],"category_scores_gemma":[0.0001919595,0.0001388052,0.00002501286,0.0002937145,0.0001851971,0.000003253098,0.0001087456,0.0001107665,0.000007588725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001663716,"about_ca_system_score_gemma":0.00005067942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004538269,"about_ca_topic_score_gemma":0.00004627784,"domain_scores_codex":[0.9987513,0.0001572631,0.0004592669,0.0003132963,0.00004580946,0.0002730459],"domain_scores_gemma":[0.9993843,0.00004788083,0.00009463535,0.0003224591,0.00005495644,0.00009579214],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001058059,0.0002820616,0.4789042,0.00005263889,0.00003258763,0.000003105162,0.0003058664,0.00009066852,0.1061642,0.2587948,0.0003253121,0.1549387],"study_design_scores_gemma":[0.001622103,0.0006634105,0.7005838,0.00001392637,0.00002728064,0.00002238419,0.0004400733,0.001095535,0.02893698,0.1624034,0.1034611,0.0007300293],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1850603,0.001920507,0.8109405,0.00007290929,0.00004945009,0.0004207853,0.00004873337,0.000007577278,0.001479252],"genre_scores_gemma":[0.9304383,0.001120013,0.06729936,0.0004831903,0.0000197875,0.0003251436,0.0002932873,0.00001082627,0.00001002814],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7453781,"threshold_uncertainty_score":0.566031,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02503040580955535,"score_gpt":0.3209507100143086,"score_spread":0.2959203042047532,"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."}}