{"id":"W2969910370","doi":"10.1002/aic.16764","title":"Multistage adaptive optimization using hybrid scenario and decision rule formulation","year":2019,"lang":"en","type":"article","venue":"AIChE Journal","topic":"Process Optimization and Integration","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematical optimization; Stochastic programming; Computer science; Robust optimization; Linear programming; Process (computing); Stochastic optimization; Optimization problem; 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":[0.0001388256,0.00009744595,0.0000991784,0.0001204457,0.00009722037,0.000117855,0.00004922013,0.00005113977,0.0001771992],"category_scores_gemma":[0.00002973119,0.00008937172,0.00002566172,0.00009568445,0.000006692556,0.0008230782,0.0000119584,0.000168908,0.0000148734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000107425,"about_ca_system_score_gemma":0.00002132963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003681648,"about_ca_topic_score_gemma":0.000001919333,"domain_scores_codex":[0.999428,0.00001110834,0.0002120407,0.00008602181,0.0001435343,0.0001192821],"domain_scores_gemma":[0.9996549,0.00002464135,0.00006690338,0.00006524614,0.0001245307,0.00006372014],"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":[0.0000196555,0.000006078777,0.0007016609,0.000006856848,0.00001032479,0.000001150718,0.0001075193,0.9880135,0.001402046,0.0000706282,0.0001284603,0.009532112],"study_design_scores_gemma":[0.0004959825,0.00002868416,0.0005492451,0.00005654443,0.00001055486,0.0000789424,0.00006356893,0.9968591,0.001290245,0.0002829366,0.0001748598,0.0001093592],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2684036,0.0001812017,0.7304285,0.000008054066,0.0002210687,0.00007822361,0.000002382474,0.00004323508,0.0006336921],"genre_scores_gemma":[0.8458019,0.0001953104,0.1538288,0.00003427821,0.00006080526,7.574931e-7,0.00001107616,0.00001958325,0.00004748089],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5773983,"threshold_uncertainty_score":0.3644473,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01104566028304686,"score_gpt":0.2271813157374354,"score_spread":0.2161356554543886,"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."}}