{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001013486,0.00009326409,0.00007599225,0.00008280973,0.00006629164,0.00004312457,0.0000399712,0.00005694813,0.00003576005],"category_scores_gemma":[0.00001140502,0.00009222349,0.00002104319,0.0001889092,0.000004356707,0.0002101788,0.000006566938,0.00006959937,0.00003213178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001667498,"about_ca_system_score_gemma":0.00001192881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000206557,"about_ca_topic_score_gemma":0.0000381682,"domain_scores_codex":[0.9994165,0.00001901332,0.0001670043,0.0001103528,0.0001143264,0.0001728103],"domain_scores_gemma":[0.9997764,0.00001254547,0.00002585743,0.0001167874,0.00002990331,0.00003846901],"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":[4.630005e-7,0.000006117648,0.00001607537,0.000004128178,0.000008894211,1.710509e-7,0.00007120277,0.9737689,0.008049648,0.00008479914,0.000114833,0.0178748],"study_design_scores_gemma":[0.0001224186,0.000009170152,0.00002400246,0.000008197339,0.00001475212,0.000008981244,0.00001850301,0.9959965,0.003541059,0.000006753383,0.0001361473,0.000113526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1658472,0.000213236,0.8328815,0.000009533142,0.0001732866,0.0001564148,3.536024e-7,0.0003821407,0.0003363363],"genre_scores_gemma":[0.9317271,0.00000629662,0.06789373,0.0000107007,0.0002393376,0.00000938587,0.00000745871,0.00002762934,0.00007842942],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7658799,"threshold_uncertainty_score":0.3760765,"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."}}