{"id":"W2615310803","doi":"10.3390/data3010008","title":"RAE: The Rainforest Automation Energy Dataset for Smart Grid Meter Data Analysis","year":2018,"lang":"en","type":"article","venue":"Data","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":83,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Thermostat; Smart meter; Smart grid; Automation; Electricity meter; Computer science; Test data; Real-time computing; Energy (signal processing); Electricity; Engineering; Power (physics); Electrical engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0006316231,0.0001453372,0.00015828,0.0001390298,0.0001177752,0.0001265948,0.002664405,0.00003905525,0.0001706509],"category_scores_gemma":[0.0001145151,0.000110522,0.00002860668,0.0004790757,0.00005548953,0.0007164886,0.001517879,0.0000456224,0.00008194229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002409798,"about_ca_system_score_gemma":0.000009687576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003395746,"about_ca_topic_score_gemma":0.003718128,"domain_scores_codex":[0.9989087,0.00002548242,0.0002451071,0.000386392,0.0001910596,0.0002432376],"domain_scores_gemma":[0.9939665,0.0001145411,0.00004326395,0.005806273,0.0000241676,0.00004521368],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000004873559,0.000006761422,0.0001018612,0.00001257009,0.000769695,7.505074e-7,0.00001311435,0.007900079,0.00001613161,0.0004704843,0.9868447,0.003859032],"study_design_scores_gemma":[0.00009180711,0.000005266448,0.0009856329,0.000001679205,0.0003149365,4.119387e-7,0.000006441621,0.4783677,0.00005575451,0.0000183615,0.5200741,0.00007788376],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"dataset","genre_scores_codex":[0.0004242579,0.0001284691,0.8305111,0.0006845305,0.001903842,0.0002618426,0.1644069,0.0003616911,0.001317327],"genre_scores_gemma":[0.04121588,0.00006346094,0.009541056,0.0006597115,0.00146912,0.00005831834,0.9467875,0.00004296672,0.0001620039],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.8209701,"threshold_uncertainty_score":0.4951173,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05918472748937553,"score_gpt":0.2833796318320338,"score_spread":0.2241949043426583,"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."}}