{"id":"W2127971672","doi":"10.1109/mper.2001.4311153","title":"Energetic Operation Planning Using Genetic Algorithms","year":2001,"lang":"en","type":"article","venue":"IEEE Power Engineering Review","topic":"Electric Power System Optimization","field":"Engineering","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Generality; Computer science; Mathematical optimization; Genetic algorithm; Convergence (economics); Parallelism (grammar); Electric power system; Class (philosophy); Nonlinear programming; Adaptation (eye); Nonlinear system; Optimization problem; Cascade; Algorithm; Power (physics); Artificial intelligence; Mathematics; Machine learning; Engineering; Parallel computing","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001900549,0.0002809554,0.0003600113,0.0001503035,0.00004199728,0.00004700354,0.0001729448,0.00008712878,0.00008306004],"category_scores_gemma":[0.00003359503,0.0003016141,0.00008100019,0.0005400882,0.000005924547,0.000193686,0.00001133406,0.0001681489,0.00006068915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001768672,"about_ca_system_score_gemma":0.00001944834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004281493,"about_ca_topic_score_gemma":1.854924e-7,"domain_scores_codex":[0.9986635,0.00002416531,0.0004755207,0.0002288924,0.0002170619,0.0003908859],"domain_scores_gemma":[0.9994544,0.00002860637,0.00004299249,0.0003160305,0.00004888963,0.0001090829],"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":[3.989754e-7,0.000005685671,0.00005923121,0.0006812313,0.000030505,0.00003026173,0.00003734238,0.993295,0.003464609,0.000008096617,0.0006088574,0.001778772],"study_design_scores_gemma":[0.0001360598,0.00002047341,0.0001495927,0.002686479,0.00006139594,0.0002833945,0.000001532961,0.9752541,0.001060123,0.000001455404,0.01992374,0.0004216387],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0100808,0.1762429,0.8105184,0.00001856156,0.001516972,0.0003697255,0.000001629727,0.0006842625,0.0005666835],"genre_scores_gemma":[0.8945104,0.05159907,0.05266836,0.0002178745,0.0004394034,0.0001224733,0.00002160317,0.000294091,0.0001266902],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8844296,"threshold_uncertainty_score":0.9999436,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01464780903386215,"score_gpt":0.2409308107605007,"score_spread":0.2262830017266385,"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."}}