{"id":"W3207034668","doi":"10.1109/tpwrd.2021.3120625","title":"Data-Driven Based Low-Voltage Distribution System Transformer-Customer Relationship Identification","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Power Delivery","topic":"Electricity Theft Detection Techniques","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"National Natural Science Foundation of China","keywords":"Distribution transformer; Transformer; Electric power distribution; Computer science; Electronic engineering; Voltage; Reliability engineering; Engineering; Electrical engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002615502,0.0002465108,0.0002173377,0.0002619542,0.0002840782,0.0001027639,0.0003114236,0.000232967,0.0001381616],"category_scores_gemma":[0.00001112265,0.0002963149,0.0001457313,0.0008973863,0.00004377989,0.0006087223,0.000001148582,0.0005174237,0.0003439757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005470323,"about_ca_system_score_gemma":0.00008130568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001425536,"about_ca_topic_score_gemma":0.00005374705,"domain_scores_codex":[0.9983019,0.00009338464,0.0004937561,0.0004307588,0.0003807693,0.0002994511],"domain_scores_gemma":[0.9985771,0.0001693178,0.0000574395,0.0009071081,0.0001859899,0.0001030746],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001746435,0.0007498073,0.00007450405,0.0009148265,0.0004647501,0.0001253914,0.000336341,0.6299145,0.3354383,0.000658392,0.008708168,0.02244042],"study_design_scores_gemma":[0.000483699,0.00005063743,0.0004112539,0.0001572045,0.0001631764,0.00005316718,0.0001199633,0.2840678,0.710398,0.0000210356,0.003603523,0.000470538],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03493948,0.00007898134,0.9597219,0.0000502483,0.0008846215,0.0002986494,0.001429193,0.001908184,0.0006887134],"genre_scores_gemma":[0.9985979,0.00006124783,0.0004216272,0.00004143483,0.00002593147,0.00008876008,0.0004723732,0.00006229575,0.0002284422],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9636584,"threshold_uncertainty_score":0.9999489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0168776219922671,"score_gpt":0.2279155458985018,"score_spread":0.2110379239062347,"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."}}