{"id":"W2534400317","doi":"","title":"Model order reduction using PSO algorithm and it's application to power systems","year":2009,"lang":"en","type":"article","venue":"International Conference on Electric Power and Energy Conversion Systems","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Electric power system; Reduction (mathematics); Particle swarm optimization; Computer science; Mathematical optimization; Nonlinear system; Power (physics); Control theory (sociology); Algorithm; Mathematics; Artificial intelligence","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":[0.0001045841,0.0001866577,0.0001909208,0.0002067421,0.0001333399,0.0001683613,0.0001172347,0.00007452784,0.00004608724],"category_scores_gemma":[0.000002046758,0.0001745272,0.00003483823,0.0001934782,0.00001761485,0.000193875,0.00002303879,0.0001229893,0.000007936994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006383036,"about_ca_system_score_gemma":0.00005345006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003834646,"about_ca_topic_score_gemma":2.065738e-7,"domain_scores_codex":[0.9988099,0.0000430038,0.0002623778,0.0003986088,0.0002808953,0.0002051946],"domain_scores_gemma":[0.9992934,0.00001281767,0.0001247755,0.0001375553,0.0002775438,0.0001539483],"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":[0.0003150038,0.000314094,0.0001392115,0.00001366052,0.0002281471,0.000003760692,0.0005774102,0.1472078,0.06143428,0.693228,0.008467646,0.08807103],"study_design_scores_gemma":[0.0003125856,0.0001282751,0.00001925938,0.00004688925,0.00001080213,0.00002490183,0.0003428948,0.993418,0.0003470664,0.0005180327,0.004627448,0.0002038029],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06516521,0.0001471786,0.9107386,0.001012965,0.001359299,0.0002230174,0.00001731964,0.00005632145,0.02128008],"genre_scores_gemma":[0.9970443,0.00005947394,0.0001599396,0.0001882431,0.0001683091,0.00002155443,0.0000252661,0.0000109111,0.002321959],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9318792,"threshold_uncertainty_score":0.7117013,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02122806948776572,"score_gpt":0.2676946429150235,"score_spread":0.2464665734272578,"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."}}