{"id":"W2578607773","doi":"10.1007/s10107-018-1339-4","title":"Two-stage linear decision rules for multi-stage stochastic programming","year":2018,"lang":"en","type":"article","venue":"Mathematical Programming","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":43,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"U.S. Department of Energy; National Science Foundation","keywords":"Mathematics; Upper and lower bounds; Linear programming; Mathematical optimization; Stochastic programming; Value (mathematics); Function (biology); Dual (grammatical number); Affine transformation; Bellman equation; Statistics; Pure 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01070137,0.001574942,0.003240915,0.0009355498,0.0005852504,0.00321063,0.002688579,0.002813874,0.005155334],"category_scores_gemma":[0.02078857,0.001666403,0.002300192,0.001137429,0.001467348,0.003358975,0.001905991,0.004743412,0.0008645951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001799479,"about_ca_system_score_gemma":0.001993095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003312581,"about_ca_topic_score_gemma":0.00370794,"domain_scores_codex":[0.9948311,0.002968919,0.0003098131,0.0006655462,0.0008283118,0.0003964264],"domain_scores_gemma":[0.9852059,0.01229521,0.0005665004,0.0005085428,0.001098132,0.0003256409],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001312627,0.0001341947,0.0005779399,0.0001733461,0.0001159422,0.0001089149,0.00009688734,0.8200045,0.0008592158,0.1474735,0.001150437,0.02917391],"study_design_scores_gemma":[0.00001707425,0.00003218059,0.00006254125,0.0000158744,0.00001852484,0.00001317228,0.000004040006,0.9629531,0.0002903244,0.03613892,0.0004406874,0.00001359992],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004776278,0.000268018,0.9930478,0.0001577058,0.00004187532,0.00005796506,0.00006287147,0.00005659801,0.001530962],"genre_scores_gemma":[0.4824359,0.0009435961,0.5035158,0.00022498,0.0001624426,0.0007930833,0.0004605385,0.0001289108,0.01133462],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01070137,"threshold_uncertainty_score":0.05659497,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1949070732070776,"score_gpt":0.4531267254404512,"score_spread":0.2582196522333736,"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."}}