{"id":"W2950492904","doi":"10.48550/arxiv.1701.04102","title":"Two-stage Linear Decision Rules for Multi-stage Stochastic Programming","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Division of Civil, Mechanical and Manufacturing Innovation; Advanced Scientific Computing Research; U.S. Department of Energy; Office of Science; National Science Foundation","keywords":"Mathematics; Linear programming; Upper and lower bounds; Mathematical optimization; Value (mathematics); Dual (grammatical number); Stochastic programming; Function (biology); Affine transformation; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00207811,0.0004942897,0.0007156085,0.0007303447,0.0008375662,0.0007107351,0.002661309,0.0004774383,0.0001361297],"category_scores_gemma":[0.002796881,0.0004651542,0.0005789561,0.0003847322,0.0002655455,0.0006013711,0.001431268,0.0005331241,0.0003037633],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001471731,"about_ca_system_score_gemma":0.0003467429,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002672706,"about_ca_topic_score_gemma":0.0003461242,"domain_scores_codex":[0.9963176,0.0001615038,0.0006740133,0.001830287,0.0004517116,0.0005649045],"domain_scores_gemma":[0.9939785,0.001009894,0.001376652,0.002372419,0.0009410843,0.0003214515],"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":[0.0002575059,0.0001286554,0.002749997,0.00002075041,0.00005194458,0.00009291764,0.000184923,0.9638483,0.000004778482,0.009966726,0.0002652026,0.02242832],"study_design_scores_gemma":[0.001648264,0.00007081756,0.0006509875,0.0001103824,0.000110279,0.000001564399,0.0003967243,0.9637191,0.00002173167,0.01553595,0.01716235,0.0005718615],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1692718,0.00008973548,0.8273107,0.00002303798,0.001128228,0.001137941,0.0003237054,0.0001293288,0.0005855535],"genre_scores_gemma":[0.8879938,0.0002766093,0.07871858,0.00002935046,0.0002549769,0.000008614855,0.0001723276,0.0000607509,0.03248501],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7485921,"threshold_uncertainty_score":0.99978,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3512985353620356,"score_gpt":0.356108318444007,"score_spread":0.004809783081971386,"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."}}