{"id":"W4413122761","doi":"10.3390/info16080676","title":"An Approximate Algorithm for Sparse Distributionally Robust Optimization","year":2025,"lang":"en","type":"article","venue":"Information","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta","keywords":"Robust optimization; Computer science; Algorithm; Mathematical optimization; Optimization algorithm; Mathematics","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":[0.001383072,0.000105975,0.0001394429,0.0003672346,0.0002671746,0.0005822357,0.000323441,0.0001017745,0.00007857809],"category_scores_gemma":[0.0005917961,0.00008915864,0.00006470882,0.0007977554,0.00003250012,0.003864923,0.00002816514,0.00005134723,0.00005636302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005861988,"about_ca_system_score_gemma":0.0001245554,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007179163,"about_ca_topic_score_gemma":0.000002034528,"domain_scores_codex":[0.9983827,0.00004677281,0.0007416327,0.0001598638,0.0005015029,0.0001674555],"domain_scores_gemma":[0.9981843,0.0001612114,0.0003054265,0.0003334688,0.0009568909,0.00005870478],"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":[0.00001650346,0.00001907541,0.0001808718,0.000002022025,0.000004150344,4.405332e-8,0.00009441646,0.6825483,0.000001755611,0.006149361,0.004990153,0.3059933],"study_design_scores_gemma":[0.000408232,0.00003198613,0.0009927356,0.000006059375,0.000009926554,0.000001180539,0.0001899905,0.9405934,0.0001128369,0.005956476,0.05160283,0.00009435419],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004520481,0.00001528569,0.9923898,0.0003394836,0.0004666053,0.0004637841,0.0002131812,0.00008501498,0.005574805],"genre_scores_gemma":[0.05510208,0.0001366515,0.9348779,0.001141001,0.0001740164,0.0002337906,0.007058103,0.00001181537,0.001264617],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.305899,"threshold_uncertainty_score":0.5614512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04056409178548765,"score_gpt":0.3444525310708437,"score_spread":0.303888439285356,"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."}}