{"id":"W1551178689","doi":"10.1109/naps.2005.1560584","title":"Generation expansion under risk using stochastic programming","year":2005,"lang":"en","type":"article","venue":"","topic":"Electric Power System Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Randomness; Stochastic programming; Computer science; Mathematical optimization; Stochastic modelling; Stochastic process; Stochastic optimization; Value (mathematics); Work (physics); Expected value; Mathematics; Engineering; Statistics","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":[0.002177946,0.0009964221,0.001169452,0.0005898604,0.000396783,0.001706159,0.0007174074,0.0008833282,0.001450474],"category_scores_gemma":[0.004050754,0.0005908912,0.001018276,0.000863623,0.001090649,0.00184037,0.001179573,0.001370388,0.0001332527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001352299,"about_ca_system_score_gemma":0.001452112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00254625,"about_ca_topic_score_gemma":0.002302167,"domain_scores_codex":[0.9984367,0.0009275856,0.00003768209,0.0001669476,0.0003125137,0.0001184253],"domain_scores_gemma":[0.9979293,0.001611825,0.0002208758,0.00005916122,0.0001172348,0.00006157158],"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":[0.00001311176,0.00001017491,0.0001063827,0.0000263503,0.00002132041,0.0000352016,0.00001834351,0.9295409,0.0001709307,0.06537186,0.0002283198,0.004457098],"study_design_scores_gemma":[0.000005121711,0.00001738475,0.00003394815,0.000007441975,0.000005343878,0.00001241721,0.000008216205,0.9465445,0.0000762133,0.05292936,0.0003542217,0.000005762103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01941674,0.0006702163,0.9745079,0.0005959413,0.00003241333,0.00003080212,0.00004551972,0.0000726537,0.004627753],"genre_scores_gemma":[0.9096339,0.001691891,0.08205052,0.0001254758,0.0001404484,0.0001829624,0.00009520014,0.00007415489,0.006005412],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00254625,"threshold_uncertainty_score":0.01151818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0236913928919971,"score_gpt":0.23718446932323,"score_spread":0.2134930764312329,"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."}}