{"id":"W2922013388","doi":"10.1007/s11081-019-09426-5","title":"Multistage stochastic capacity planning of partially upgraded bitumen production with hybrid solution method","year":2019,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Energy, Environment, and Transportation Policies","field":"Energy","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Resources Canada","keywords":"Variable (mathematics); Mathematical optimization; Asphalt; Computer science; Set (abstract data type); Production (economics); Production planning; Random variable; Stochastic modelling; Mathematics; Statistics","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":[0.00008115036,0.0001031756,0.0001251889,0.00008416997,0.00003627523,0.0000110201,0.00002742881,0.00003063492,0.00003029274],"category_scores_gemma":[0.00001265408,0.00009931209,0.00001673534,0.00008938513,0.00001772922,0.0001556765,0.000004641578,0.00005419848,0.000001252977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001830346,"about_ca_system_score_gemma":0.000005706362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001312268,"about_ca_topic_score_gemma":0.00001262265,"domain_scores_codex":[0.999446,0.00001343908,0.0001559299,0.0001580856,0.0001044295,0.0001220538],"domain_scores_gemma":[0.9997528,0.00001867369,0.00006940128,0.0001000554,0.00001925309,0.00003980996],"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":[0.00001368339,0.00001388564,0.0005265149,0.00004930965,0.00002519188,4.064823e-7,0.0004248814,0.9862183,0.01095407,0.001657238,0.000002097909,0.0001143952],"study_design_scores_gemma":[0.0004352925,0.00004230453,0.005402969,0.00005839434,0.00002936515,0.000006084534,0.00006144053,0.9802112,0.01341726,0.000008300117,0.0001797996,0.0001475705],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2657437,0.00002212774,0.7338282,0.00002593212,0.00007817014,0.00008424377,0.000003071723,0.00005618369,0.0001584033],"genre_scores_gemma":[0.929382,0.0000147443,0.07031593,0.00000853897,0.00002637894,0.0000112024,0.00004457491,0.0000195029,0.0001771548],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6636383,"threshold_uncertainty_score":0.404983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01025784337057726,"score_gpt":0.2062673190846438,"score_spread":0.1960094757140666,"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."}}