{"id":"W2560424504","doi":"10.1109/epec.2016.7771719","title":"Dynamic threshold algorithm with simplified appliance identification for smart meter privacy","year":2016,"lang":"en","type":"article","venue":"","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Smart meter; Identification (biology); Computer science; Smart grid; Compensation (psychology); Metre; Key (lock); Algorithm; Differential privacy; Information privacy; Real-time computing; Data mining; Computer security; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008011211,0.0005230894,0.0007039641,0.000607644,0.0004858333,0.001319835,0.001037939,0.0008765302,0.001924925],"category_scores_gemma":[0.004198683,0.0002248932,0.0005813384,0.0008742831,0.0005485163,0.001974571,0.000749669,0.001046797,0.0007208803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005265468,"about_ca_system_score_gemma":0.001333781,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001729683,"about_ca_topic_score_gemma":0.001534171,"domain_scores_codex":[0.9988604,0.0002191002,0.0000965834,0.0002533138,0.0004620282,0.0001087129],"domain_scores_gemma":[0.9987032,0.0005651275,0.0001066782,0.0003069271,0.0002787618,0.0000393497],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006136627,0.0002309951,0.003102978,0.0001504848,0.0001289691,0.0002364811,0.0002418461,0.2676085,0.07169827,0.07012965,0.004064622,0.5817935],"study_design_scores_gemma":[0.0000217915,0.00009963702,0.0006658683,0.000008060695,0.00002120729,0.0002095136,0.00002261241,0.9735504,0.01145517,0.01047776,0.003447813,0.00002001933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008945954,0.0001309387,0.9894544,0.00009165052,0.00005187517,0.00003093171,0.00001790192,0.0003225237,0.0009538343],"genre_scores_gemma":[0.5254869,0.000293209,0.4696833,0.0002288262,0.000148193,0.0001297597,0.0001949372,0.00009177938,0.003743113],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001924925,"threshold_uncertainty_score":0.006439567,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009908898596534379,"score_gpt":0.2154926689316158,"score_spread":0.2055837703350814,"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."}}