{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.01002301,0.0001647186,0.000272139,0.0006428183,0.001562221,0.0006676143,0.002879352,0.00006829587,0.0001137035],"category_scores_gemma":[0.0002185678,0.0001639543,0.00006198025,0.002502381,0.00005540924,0.0007523877,0.002521401,0.001092204,0.0000232981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004824514,"about_ca_system_score_gemma":0.0006671837,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000341527,"about_ca_topic_score_gemma":0.000002012028,"domain_scores_codex":[0.9912688,0.004613148,0.0003762804,0.0008506275,0.001958167,0.0009329977],"domain_scores_gemma":[0.9972515,0.0008885908,0.0001080894,0.001289888,0.0003221424,0.0001397692],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008376629,0.001019471,0.0007629625,0.0001192257,0.0013916,0.004247724,0.004868701,0.2705478,0.1707177,0.1035502,0.4180826,0.02385432],"study_design_scores_gemma":[0.0005236122,0.000193029,0.00003812894,0.000009548901,0.00000607354,0.0002713453,0.00005561438,0.9750265,0.0005317767,0.0005687034,0.02257624,0.000199458],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001706605,0.002239132,0.991705,0.0001455575,0.001949044,0.0004304413,0.00002239956,0.00007271759,0.001729114],"genre_scores_gemma":[0.9847466,0.000007438875,0.01317193,0.0001498207,0.000471218,0.00006714034,0.00007135383,0.00002564982,0.001288862],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.98304,"threshold_uncertainty_score":0.9997376,"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."}}