{"id":"W2907537524","doi":"10.3166/ejee.19.19-30","title":"Improved canis rufus floridanus optimization algorithm for reduction of real power loss and maximization of static voltage stability margin","year":2017,"lang":"en","type":"article","venue":"European Journal of Electrical Engineering","topic":"Power Systems and Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Reduction (mathematics); Margin (machine learning); Maximization; Stability (learning theory); Canis; Computer science; Power (physics); Mathematical optimization; Algorithm; Control theory (sociology); Mathematics; Biology; Physics; Ecology; Artificial intelligence; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003403127,0.0008007464,0.0007935242,0.0006341433,0.0004741941,0.0006644457,0.0008494427,0.0007684309,0.005309721],"category_scores_gemma":[0.0005638018,0.000216751,0.0004758804,0.0003646403,0.000248514,0.0004344437,0.0005270696,0.0005102333,0.0006222388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004908347,"about_ca_system_score_gemma":0.0006848658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006506784,"about_ca_topic_score_gemma":0.006662024,"domain_scores_codex":[0.999869,0.0000302383,0.000005305674,0.00002396024,0.00004888089,0.00002259948],"domain_scores_gemma":[0.9999199,0.00002794241,0.000008120731,0.00000738537,0.00003071054,0.00000594686],"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":[0.0002703791,0.0001183355,0.0005799978,0.0001705665,0.00005221264,0.0001158129,0.00007534045,0.6917647,0.007384827,0.02049372,0.007720475,0.2712536],"study_design_scores_gemma":[0.00001797221,0.00004994224,0.0001485788,0.00001202894,0.000009362808,0.00002743536,0.000007414082,0.9953177,0.001084603,0.00112847,0.002191817,0.000004720855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03457056,0.001362489,0.9342022,0.0002541117,0.0002276557,0.00007067097,0.00008091031,0.0007901925,0.02844114],"genre_scores_gemma":[0.6572897,0.000635558,0.3190609,0.000157429,0.0001210343,0.0002237786,0.0002202777,0.000141174,0.02215016],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006506784,"threshold_uncertainty_score":0.01776284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008279880844455252,"score_gpt":0.1954513166696192,"score_spread":0.187171435825164,"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."}}