{"id":"W4225397848","doi":"10.1016/j.epsr.2022.108033","title":"Smart Meter Data Masking Using Conditional Generative Adversarial Networks","year":2022,"lang":"en","type":"article","venue":"Electric Power Systems Research","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Masking (illustration); Smart meter; Standard deviation; Statistics; Computer science; Gaussian; Mathematics; Pattern recognition (psychology); Data mining; Algorithm; Artificial intelligence; Smart grid; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009959162,0.0006539199,0.000592606,0.0003957109,0.0002253102,0.0005209931,0.0008412384,0.001059544,0.002762975],"category_scores_gemma":[0.002463029,0.0005175809,0.0008388606,0.0003398235,0.0006130578,0.0008935035,0.001272979,0.001545594,0.0009684653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004129932,"about_ca_system_score_gemma":0.0004278914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001670231,"about_ca_topic_score_gemma":0.002360425,"domain_scores_codex":[0.9996524,0.0001028592,0.00001137133,0.00007959622,0.0001130095,0.00004081394],"domain_scores_gemma":[0.9989674,0.000638422,0.00008798526,0.0001584567,0.0001148646,0.00003283167],"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":[0.0003292641,0.00006302437,0.001002239,0.00008729914,0.00008191522,0.0001923491,0.00008524897,0.8390716,0.01701763,0.01908179,0.002574263,0.1204133],"study_design_scores_gemma":[0.000002993754,0.0000121381,0.0001199434,0.000004183426,0.000007548616,0.00002877065,0.000003052157,0.9946142,0.002094964,0.002744111,0.0003634639,0.000004613153],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01248866,0.0001053816,0.9852381,0.0001690736,0.00006717497,0.00002097805,0.00006025315,0.0004976457,0.001352657],"genre_scores_gemma":[0.7701105,0.0003420691,0.2146952,0.0004237086,0.0001252067,0.00007169023,0.0004422243,0.0002698401,0.01351947],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002762975,"threshold_uncertainty_score":0.009243071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1773604991656374,"score_gpt":0.4017702293432808,"score_spread":0.2244097301776435,"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."}}