{"id":"W2092275080","doi":"10.5539/mas.v6n2p22","title":"A Fuzzy Based Solution for Improving Power Quality in Electric Railway Networks","year":2012,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Optimal Power Flow Distribution","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"AC power; Compensation (psychology); Supply network; Voltage; Traction (geology); Capacitor; Computer science; Fuzzy logic; Traction substation; Electric power; Automotive engineering; Power (physics); Control theory (sociology); Electrical engineering; Engineering; Mechanical engineering; Control (management)","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":[0.001569956,0.0001392088,0.0001384206,0.0001461913,0.0001326507,0.00005747201,0.0002361893,0.00008282314,0.000004107827],"category_scores_gemma":[0.00005672532,0.0001500697,0.00003603744,0.0007488288,0.00006970122,0.0003587595,0.00003388556,0.0001621291,0.00001137924],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004489807,"about_ca_system_score_gemma":0.00005622447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001071256,"about_ca_topic_score_gemma":0.000007960955,"domain_scores_codex":[0.9984084,0.00001022535,0.000232891,0.0002605855,0.0002579327,0.0008299859],"domain_scores_gemma":[0.9995108,0.00005328452,0.0000398543,0.0002343893,0.00003840806,0.0001232322],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003197528,0.00005471478,0.0004787151,0.00003033866,0.000001594049,9.618816e-8,0.0001619628,0.09811501,0.8844146,0.002719311,0.00006429242,0.01392743],"study_design_scores_gemma":[0.0003380214,0.00001498946,0.008667197,0.000003544185,0.000003236242,3.468833e-7,0.000009129627,0.980728,0.009640143,0.0003571193,0.00004343337,0.0001948939],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1334706,0.0001243795,0.8631255,0.00001568002,0.0002364495,0.0003735597,0.000005901552,0.0001700867,0.002477886],"genre_scores_gemma":[0.9953807,6.777751e-7,0.00437247,0.00004590433,0.00005275498,0.0001159722,0.00001053429,0.00001740454,0.000003605059],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8826129,"threshold_uncertainty_score":0.6119664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01371952921182658,"score_gpt":0.2460724109619731,"score_spread":0.2323528817501465,"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."}}