{"id":"W4205712074","doi":"10.5539/jmr.v14n1p46","title":"Relationship between Event Prevalence Rate and Gini Coefficient of Predictive Model","year":2022,"lang":"en","type":"article","venue":"Journal of Mathematics Research","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Statistic; Mathematics; Gini coefficient; Statistics; Event (particle physics); Econometrics; Measure (data warehouse); Inequality; Data mining; Computer science; Economic inequality","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.01773916,0.00008283013,0.0003228424,0.0004166928,0.001069121,0.000007153632,0.0003993837,0.00008452787,0.0001853672],"category_scores_gemma":[0.005294018,0.00007022233,0.00006268663,0.0004236844,0.0001857724,0.00009691934,0.0004508875,0.002330451,0.00001344411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004107956,"about_ca_system_score_gemma":0.00111878,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003369281,"about_ca_topic_score_gemma":0.00000797613,"domain_scores_codex":[0.9943253,0.002218381,0.001430331,0.0001353724,0.001492001,0.0003985782],"domain_scores_gemma":[0.9877434,0.009579862,0.0008308369,0.0002661036,0.001387084,0.0001927648],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003067867,0.0006744502,0.7911339,0.006810791,0.00006796097,0.00002032931,0.1086839,0.05620163,0.0004002891,0.03063758,0.004433988,0.0006284816],"study_design_scores_gemma":[0.0005363924,0.002024385,0.05916202,0.001744889,0.00008079238,0.00002444393,0.09964975,0.60208,0.0004738567,0.2336816,0.0003436623,0.0001980926],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9816571,0.0004070266,0.01474575,0.001555124,0.0001417254,0.0008285182,0.00007835072,0.000007392779,0.0005790399],"genre_scores_gemma":[0.9966471,0.00008078561,0.002134851,0.00002030327,0.0001024689,0.0000518639,0.000001136176,0.0000188984,0.0009425742],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7319719,"threshold_uncertainty_score":0.9999712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5758864769581103,"score_gpt":0.6063297702660546,"score_spread":0.03044329330794426,"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."}}