{"id":"W3171241085","doi":"10.1016/j.cor.2021.105398","title":"Chance constrained unit commitment approximation under stochastic wind energy","year":2021,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Electric Power System Optimization","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":false,"ca_institutions":"Laurentian University","funders":"National Science Foundation","keywords":"Mathematical optimization; Wind power; Computer science; Intermittency; Power system simulation; Time horizon; Renewable energy; Scheduling (production processes); Electric power system; Power (physics); Mathematics; Engineering; Electrical engineering","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":[0.0003383744,0.000130671,0.0001544297,0.0002562391,0.0003065986,0.0002488767,0.0001946612,0.00007950063,0.00008658275],"category_scores_gemma":[0.00004622558,0.0001477503,0.00003090591,0.0009880709,0.00006628122,0.0002021761,0.0000717531,0.0002355997,0.00005697849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002440824,"about_ca_system_score_gemma":0.0002060794,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002864658,"about_ca_topic_score_gemma":0.0000857721,"domain_scores_codex":[0.9984596,0.0002498895,0.0002594453,0.0002486166,0.0004169095,0.0003654953],"domain_scores_gemma":[0.9988336,0.0001668849,0.000009470937,0.0003663294,0.0005181108,0.0001056309],"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":[0.000001663936,0.00004065723,0.000003223494,0.00002088495,0.00003531141,0.000005396787,0.0002261297,0.9748682,0.001810143,0.01889388,0.001224936,0.002869609],"study_design_scores_gemma":[0.0003527183,0.0000345831,0.00008821979,0.00005566932,0.000004808927,0.00002779282,0.0001407337,0.9955988,0.002829589,0.000134776,0.0005890122,0.0001432878],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007529653,0.0005442149,0.9887283,0.0005054207,0.0003610852,0.0002766354,0.000007607593,0.0001773678,0.001869703],"genre_scores_gemma":[0.9886048,0.00005003042,0.01011985,0.00006604966,0.0001189804,0.00007468931,0.0002170678,0.00003217464,0.0007163823],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9810751,"threshold_uncertainty_score":0.6025082,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04731758283479383,"score_gpt":0.3043130941267354,"score_spread":0.2569955112919415,"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."}}