{"id":"W2949295428","doi":"","title":"Privacy-Optimal Strategies for Smart Metering Systems with a Rechargeable Battery","year":2015,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Smart Grid Security and Resilience","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; University of Toronto","funders":"","keywords":"Computer science; Markov decision process; Mutual information; Battery (electricity); Metering mode; Private information retrieval; Load profile; Markov process; Metric (unit); Inference; Demand response; Smart grid; Process (computing); Mathematical optimization; Power (physics); Computer security; Engineering; Artificial intelligence; Electrical engineering; Electricity; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002368247,0.0003762634,0.0004369188,0.0001934311,0.0001030716,0.0001800498,0.0006162651,0.0003170898,0.00001118093],"category_scores_gemma":[0.00001407354,0.000384967,0.0001207902,0.0002200021,0.00008431552,0.0003721883,0.0003025369,0.0004848269,0.00002506093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002382925,"about_ca_system_score_gemma":0.0002018977,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001110411,"about_ca_topic_score_gemma":0.00003933224,"domain_scores_codex":[0.9986238,0.00004195813,0.0001896628,0.0006018741,0.00008862735,0.0004540274],"domain_scores_gemma":[0.9988722,0.00007894276,0.00008964675,0.0006347009,0.0001475791,0.0001769125],"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":[0.00007988863,0.00002037642,0.0003147978,0.0008621473,0.0001733894,0.00009598899,0.0004034753,0.9943483,0.0001202719,0.002416688,0.001155418,0.000009206429],"study_design_scores_gemma":[0.000644486,0.000130394,0.00005799563,0.0004619249,0.0001599468,0.00001978813,0.001633135,0.9904123,0.0002344035,0.001549842,0.003963808,0.000731973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7273093,0.0002662136,0.2683796,0.000007470073,0.001208089,0.0005777552,0.00006576232,0.0004626969,0.001723029],"genre_scores_gemma":[0.9982207,0.0001136609,0.0008270914,0.000007619024,0.0001904329,0.0000114712,0.00003944455,0.0000585327,0.0005310566],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2709113,"threshold_uncertainty_score":0.9998602,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0778056945144661,"score_gpt":0.18056059791527,"score_spread":0.1027549034008039,"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."}}