{"id":"W4287558439","doi":"10.48550/arxiv.2012.01013","title":"Optimal Dynamic Pricing for Binary Demands in Smart Grids: A Fair and\\n Privacy-Preserving Strategy","year":2020,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Mathematical optimization; Binary number; Markov chain; Operator (biology); Population; Smart grid; Markov process; Operations research; Distributed computing; Mathematics; 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":[0.001948207,0.0008487146,0.001196821,0.0003730207,0.0006769978,0.001902041,0.001272762,0.001595026,0.003094279],"category_scores_gemma":[0.004154689,0.0004764871,0.0007748444,0.0006194137,0.001807974,0.002327309,0.001627948,0.001545948,0.0003016766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00195191,"about_ca_system_score_gemma":0.001970528,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003801972,"about_ca_topic_score_gemma":0.002794843,"domain_scores_codex":[0.9989738,0.0004643035,0.00003221712,0.0001504675,0.0001735851,0.0002055332],"domain_scores_gemma":[0.9981055,0.001272748,0.0001619763,0.0001543986,0.0001391933,0.0001661124],"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":[0.000229284,0.00008686768,0.0004557488,0.0000606174,0.00003414483,0.0001540007,0.0001091304,0.8598037,0.001574212,0.1227123,0.002278562,0.01250134],"study_design_scores_gemma":[0.00001555563,0.00001882489,0.00003998881,0.000004023092,0.000003970295,0.0000141595,0.00001550624,0.9708765,0.0001990069,0.02851202,0.0002945107,0.000005873628],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06243268,0.0002731122,0.9263322,0.001320592,0.00008674856,0.0001022647,0.0001416062,0.0002076446,0.009103166],"genre_scores_gemma":[0.9546789,0.000215134,0.04073758,0.0001722446,0.00005073651,0.00006808949,0.00006675017,0.00004066585,0.003969872],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003801972,"threshold_uncertainty_score":0.01416212,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05005840912993398,"score_gpt":0.1892807757153442,"score_spread":0.1392223665854102,"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."}}