{"id":"W2244981840","doi":"10.1109/epec.2015.7379933","title":"A proof-of-concept approach to Unit Commitment using the theory of complementarity","year":2015,"lang":"en","type":"article","venue":"","topic":"Electric Power System Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Power system simulation; Mathematical optimization; Solver; Complementarity (molecular biology); Computer science; Grid; Integer programming; Complementarity theory; Proof of concept; Power (physics); Electric power system; Mathematics; Nonlinear system","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":[0.005159738,0.002169448,0.001236957,0.001382028,0.001136334,0.003501944,0.003521619,0.002022898,0.03412038],"category_scores_gemma":[0.01371589,0.001189889,0.002580011,0.001850338,0.00348536,0.003793241,0.003533562,0.00627195,0.007912553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001578994,"about_ca_system_score_gemma":0.002859756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001001541,"about_ca_topic_score_gemma":0.0008767829,"domain_scores_codex":[0.9969684,0.001017763,0.0001097435,0.0003618784,0.00137254,0.0001696851],"domain_scores_gemma":[0.9953769,0.002826262,0.0002497447,0.0005939119,0.0008407158,0.0001123743],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000027404,0.00009275377,0.00006257609,0.0005066476,0.00004726375,0.0001708359,0.00008604368,0.02918423,0.00216643,0.9140909,0.01053754,0.0430274],"study_design_scores_gemma":[0.0001021666,0.0002632451,0.00008359488,0.0006141316,0.00003581757,0.0005628378,0.00008097164,0.2025263,0.004466803,0.7037241,0.08746932,0.00007072488],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0003170219,0.0003700409,0.9827806,0.0006212305,0.0002333494,0.0001611153,0.00006673229,0.0002229217,0.01522701],"genre_scores_gemma":[0.05899499,0.002031307,0.9236013,0.00106119,0.0005394971,0.001120971,0.0002139126,0.0003681298,0.01206869],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03412038,"threshold_uncertainty_score":0.114144,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08519581602566759,"score_gpt":0.2708347847836102,"score_spread":0.1856389687579426,"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."}}