{"id":"W2027079130","doi":"10.2316/journal.203.2006.3.203-3364","title":"A BIBLIOGRAPHICAL SURVEY OF EVOLUTIONARY COMPUTATION APPLICATIONS IN POWER SYSTEMS (1994–2003)","year":2006,"lang":"en","type":"article","venue":"International Journal of Power and Energy Systems","topic":"Electric Power System Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computation; Electric power system; Computer science; Evolutionary computation; Power (physics); Operations research; Engineering; Artificial intelligence; Algorithm; Physics; Thermodynamics","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":[0.0004720959,0.0001472612,0.000324585,0.002097295,0.00002179097,0.00007316485,0.0002149222,0.0001187912,0.000003677847],"category_scores_gemma":[0.00002820942,0.0001393511,0.00005602919,0.001818937,0.00003635093,0.0002668765,0.00001765594,0.0001209486,7.198697e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001280683,"about_ca_system_score_gemma":0.00005669182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002547699,"about_ca_topic_score_gemma":0.00006672554,"domain_scores_codex":[0.9980211,0.000141124,0.001034258,0.0001231113,0.0005287395,0.0001516673],"domain_scores_gemma":[0.998384,0.0001380424,0.0003459147,0.00008866736,0.0009829147,0.00006044646],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00005014246,0.0001639811,0.1018213,0.00005716842,0.0002485577,0.00002225904,0.0001110221,0.8815702,0.0004215627,0.007935669,0.007483183,0.0001149541],"study_design_scores_gemma":[0.001966829,0.0002151781,0.543523,0.0006116633,0.00003863578,0.000824253,0.0002706386,0.4385689,0.00007673366,0.0002128125,0.01318884,0.0005025066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1652595,0.02241835,0.798599,0.00003839715,0.007042884,0.0002834624,0.0001444186,0.00009198052,0.00612197],"genre_scores_gemma":[0.9993382,0.000247444,0.0001631042,0.000003720716,0.000087802,0.00001525295,0.00006540802,0.00002236445,0.00005673851],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8340786,"threshold_uncertainty_score":0.5682573,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00545389684011718,"score_gpt":0.2147619167965792,"score_spread":0.209308019956462,"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."}}