{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009547158,0.001259587,0.0007638478,0.002053969,0.0005506914,0.0008721706,0.001670999,0.001361524,0.005366616],"category_scores_gemma":[0.003028319,0.0003302094,0.000850958,0.003171445,0.000306077,0.001079162,0.001013399,0.001329115,0.005786486],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008349065,"about_ca_system_score_gemma":0.000949261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02191337,"about_ca_topic_score_gemma":0.04093475,"domain_scores_codex":[0.9991562,0.0001304754,0.0001054034,0.000156939,0.0003347509,0.0001162679],"domain_scores_gemma":[0.998619,0.0002303025,0.0001532149,0.0004414895,0.0004415169,0.0001145479],"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":[0.0003192901,0.0003800787,0.01381166,0.0005090847,0.0001560001,0.0002486734,0.0001192508,0.01029009,0.002733951,0.00150855,0.9405478,0.02937548],"study_design_scores_gemma":[0.001031935,0.0002511285,0.1069672,0.0002428116,0.0001056381,0.0006847481,0.0006341189,0.07342997,0.01417266,0.005465047,0.7968237,0.0001911415],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.02520822,0.0003146418,0.003472362,0.0007229964,0.0001735152,0.0002160768,0.9579998,0.007145436,0.004747053],"genre_scores_gemma":[0.02031715,0.00009642613,0.006290035,0.0001109131,0.00003159305,0.0001735566,0.9717103,0.0002364747,0.001033479],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02191337,"threshold_uncertainty_score":0.04357165,"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."}}