{"id":"W46909155","doi":"","title":"OPTIMAL ECONOMIC AND ENVIRONMENTAL OPERATION OF ELECTRIC POWER SYSTEMS VIA MODERN META-HEURISTIC OPTIMIZATION ALGORITHMS","year":2012,"lang":"en","type":"article","venue":"Library and Archives Canada (Government of Canada)","topic":"Electric Power System Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Meta heuristic; Heuristic; Computer science; Power (physics); Mathematical optimization; Algorithm; Mathematics; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00001828252,0.0001635662,0.0002715025,0.00003708649,0.00005535761,0.00001609046,0.00007591106,0.00003010059,0.00002248323],"category_scores_gemma":[8.304923e-7,0.0001655873,0.00001991005,0.00003682954,0.00001904633,0.000519865,0.00002986552,0.00006233335,3.257056e-9],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000251476,"about_ca_system_score_gemma":0.0001262991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003745929,"about_ca_topic_score_gemma":0.00009570878,"domain_scores_codex":[0.998845,0.00004412787,0.0003149598,0.0001321806,0.0004400745,0.0002236519],"domain_scores_gemma":[0.9995698,0.00007153558,0.0001014859,0.0001142829,2.656318e-7,0.0001426377],"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":[0.00002665167,0.00001205539,0.001347032,0.00008762709,0.0002516285,0.000001665859,0.00007384437,0.9868162,0.009757634,0.001187669,0.00005807566,0.0003799736],"study_design_scores_gemma":[0.000166504,0.00003672878,0.0009693677,0.0000103589,0.00009846092,0.00001440075,0.0001263278,0.9765716,0.02169601,0.000006827725,0.0001454645,0.000158015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2075758,0.01913006,0.7391497,0.0001672448,0.001046711,0.001059507,0.0005243029,0.00007355426,0.03127304],"genre_scores_gemma":[0.9964796,0.000299904,0.002887897,0.00002009149,0.00002325058,0.00001364548,0.00002188116,0.00002764576,0.0002260418],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7889038,"threshold_uncertainty_score":0.6752454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002471509256770391,"score_gpt":0.122536034903917,"score_spread":0.1200645256471466,"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."}}