{"id":"W4392373677","doi":"10.54254/2755-2721/44/20230249","title":"Distributionally Robust Optimization methods on robust medical diagnosis systems","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Robust optimization; Computer science; Robustness (evolution); Outlier; Machine learning; Artificial intelligence; Domain (mathematical analysis); Partition (number theory); Optimization problem; Data mining; Mathematical optimization; Algorithm; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001372945,0.0001529073,0.0001925787,0.0002622973,0.0001131759,0.0004712074,0.0001767214,0.0001083022,0.0001335479],"category_scores_gemma":[0.0004071261,0.0001249366,0.00004994195,0.000669553,0.00003014125,0.0001517944,0.00005112845,0.0001635639,0.00004166463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000424695,"about_ca_system_score_gemma":0.00007980736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004509768,"about_ca_topic_score_gemma":1.739796e-7,"domain_scores_codex":[0.9978726,0.00004650732,0.0004524102,0.0004054875,0.001062425,0.000160593],"domain_scores_gemma":[0.9973906,0.002171649,0.00005362851,0.0001159347,0.0001164028,0.0001518312],"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":[0.000004255935,0.00001288171,0.00004371777,0.000008141009,0.00001759278,0.000005060472,0.00003081491,0.8134245,0.000001481695,0.1645752,0.0008849507,0.02099143],"study_design_scores_gemma":[0.0001206633,0.00001512354,0.0006227203,0.00004775912,0.00001032715,0.00001974002,0.00002727214,0.9890209,0.000007232082,0.0020881,0.007879053,0.0001410488],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001201345,0.0006620521,0.9954272,0.0005305933,0.0006075625,0.000140571,0.00002665491,0.0001576722,0.001246361],"genre_scores_gemma":[0.7818279,0.0003818872,0.2166243,0.0001386302,0.0004330683,0.0001430939,0.0002878159,0.00003301393,0.0001303233],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7806265,"threshold_uncertainty_score":0.5094765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04710049864942625,"score_gpt":0.3401294191023163,"score_spread":0.2930289204528901,"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."}}