{"id":"W2303625854","doi":"10.14288/1.0135659","title":"Performance of a sampling stochastic dynamic programming algorithm with various inflow scenario generation methods","year":2015,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Water resources management and optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Inflow; Computer science; Dynamic programming; Algorithm; Sampling (signal processing); Stochastic programming; Mathematical optimization; Mathematics; Filter (signal processing); Computer vision","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004904056,0.0009690131,0.001103879,0.00102848,0.0006469166,0.001152811,0.00125812,0.001260691,0.001854729],"category_scores_gemma":[0.01152076,0.0005087318,0.0006835554,0.000971616,0.0005810158,0.001338243,0.0009232888,0.001114099,0.0002378806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001658149,"about_ca_system_score_gemma":0.003071422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03155803,"about_ca_topic_score_gemma":0.01769281,"domain_scores_codex":[0.9982144,0.001055127,0.0001095024,0.0002034831,0.0002607475,0.0001568789],"domain_scores_gemma":[0.9912853,0.006706965,0.0004045058,0.0003547955,0.0009704368,0.0002780844],"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":[0.0001270433,0.00008339332,0.001307225,0.00002247448,0.00003225399,0.00001602745,0.00001735973,0.9810369,0.0001601505,0.00109301,0.0002335304,0.01587068],"study_design_scores_gemma":[0.00001140978,0.00001746225,0.00008260362,0.000001334894,0.00000215651,0.000002055501,0.000003374229,0.9995565,0.00009167529,0.0001994947,0.00003032388,0.000001691427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5800458,0.0005346095,0.4063648,0.0008337397,0.0001113308,0.0003814722,0.0005664606,0.001915265,0.009246577],"genre_scores_gemma":[0.8521554,0.000122104,0.1457576,0.0001141426,0.00002059798,0.0001871461,0.0006750322,0.00009759243,0.0008703753],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03155803,"threshold_uncertainty_score":0.06274867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01407094024044615,"score_gpt":0.1933628430402758,"score_spread":0.1792919027998297,"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."}}