{"id":"W4246202759","doi":"10.24200/tjer.vol15iss2pp1-13","title":"Technical Loss Reduction in Rural Areas - The Case of Saih Al Khairat","year":2018,"lang":"en","type":"article","venue":"The Journal of Engineering Research [TJER]","topic":"Electricity Theft Detection Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Reduction (mathematics); Power flow; Operator (biology); Power (physics); Voltage; Electric power system; Reliability engineering; Computer science; Engineering; Electrical engineering; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003382876,0.0004984292,0.0002163315,0.0005544149,0.001135073,0.000758972,0.0006713815,0.000717842,0.002180388],"category_scores_gemma":[0.0005607936,0.0001534825,0.0002460869,0.000870844,0.0005846669,0.0006264046,0.0004825843,0.0002635095,0.0001719258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001267427,"about_ca_system_score_gemma":0.0006092373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02233587,"about_ca_topic_score_gemma":0.04434612,"domain_scores_codex":[0.9997495,0.00009738957,0.000004044678,0.00002028415,0.00003556024,0.00009324635],"domain_scores_gemma":[0.9996899,0.0001204833,0.00006512822,0.00001735106,0.00005870823,0.00004827922],"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":[0.002607734,0.002270892,0.1347892,0.0006457492,0.0002821843,0.04189744,0.004292335,0.6098182,0.0468455,0.01824078,0.005361534,0.1329485],"study_design_scores_gemma":[0.0005153054,0.004024261,0.2636169,0.0001040413,0.0003727088,0.006747218,0.03586752,0.6436378,0.01784129,0.01068232,0.01647376,0.0001167765],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9945747,0.00006252599,0.001473574,0.0001867564,0.000006165535,0.00003261505,0.00003848197,0.00002232228,0.003602901],"genre_scores_gemma":[0.9979029,0.00006922237,0.0006788223,0.000009849603,0.000004111309,0.000008344483,0.00002072931,0.000002648629,0.001303324],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02233587,"threshold_uncertainty_score":0.04441178,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0211324594820271,"score_gpt":0.3195151178614432,"score_spread":0.2983826583794161,"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."}}