{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001445734,0.0009042627,0.001166839,0.0006157447,0.0003278383,0.001292262,0.001052727,0.001376211,0.002701098],"category_scores_gemma":[0.001681277,0.0006636003,0.001149085,0.0007915112,0.0007471527,0.0009675711,0.001043035,0.001216835,0.0002291757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008529043,"about_ca_system_score_gemma":0.0009041387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00743613,"about_ca_topic_score_gemma":0.003898337,"domain_scores_codex":[0.9992674,0.0003563301,0.00003076626,0.0001023696,0.0001631972,0.00007997241],"domain_scores_gemma":[0.9986713,0.0009215262,0.0001261372,0.0000594142,0.0001647435,0.00005680356],"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":[0.00001047938,0.00001071606,0.00005678749,0.00001099098,0.00001083016,0.00002371189,0.00000531642,0.9945411,0.0002214359,0.002908568,0.00006602956,0.002134144],"study_design_scores_gemma":[0.00000245253,0.000005661214,0.00001348045,0.000001027273,0.00000105905,0.000002066819,0.000001134301,0.9992868,0.00005437791,0.0005805728,0.00004978105,0.000001415741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02595934,0.0001971016,0.9688578,0.0002102946,0.00003221259,0.00007102781,0.00007482911,0.000146279,0.004451161],"genre_scores_gemma":[0.8471385,0.0001673199,0.1494858,0.00008396213,0.00003300099,0.0002480765,0.0001322189,0.00004968992,0.002661416],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00743613,"threshold_uncertainty_score":0.01478571,"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."}}