{"id":"W6939136402","doi":"10.60692/jm233-f1477","title":"The Building Data Genome Project 2, energy meter data from the ASHRAE Great Energy Predictor III competition","year":2020,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Clinical Laboratory Practices and Quality Control","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"ASHRAE 90.1; Benchmarking; Data set; Range (aeronautics); Energy (signal processing); Metre; Measure (data warehouse); Electricity meter","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":[0.002086053,0.001029707,0.0008687886,0.002695097,0.000688591,0.001657323,0.001462729,0.00149856,0.01011634],"category_scores_gemma":[0.009649352,0.0003815257,0.0007472652,0.006906467,0.0003485579,0.0007671491,0.001869494,0.00146761,0.01078795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001015868,"about_ca_system_score_gemma":0.002451191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04009495,"about_ca_topic_score_gemma":0.06258892,"domain_scores_codex":[0.9979084,0.0004330674,0.0001379591,0.0005326429,0.000771505,0.0002165268],"domain_scores_gemma":[0.9956784,0.001104029,0.0004262388,0.0008104237,0.001499354,0.000481665],"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":[0.0004544316,0.0002100722,0.03543639,0.0007729472,0.0001854595,0.0001576807,0.0001683525,0.003768438,0.001184455,0.002272123,0.9322729,0.02311676],"study_design_scores_gemma":[0.0003767172,0.0001690897,0.1337414,0.0003465002,0.0001053196,0.000254003,0.0004532044,0.007857656,0.002463749,0.003422909,0.8507054,0.0001039364],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.006040384,0.00017141,0.0008984571,0.0002909419,0.00006374862,0.00003293427,0.9897311,0.0007126558,0.002058469],"genre_scores_gemma":[0.004448435,0.0000456771,0.001491265,0.00006514676,0.000009965124,0.00005263036,0.9931982,0.00008743868,0.0006013179],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04009495,"threshold_uncertainty_score":0.07972312,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1927382775548779,"score_gpt":0.3185277272159626,"score_spread":0.1257894496610847,"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."}}