{"id":"W3033693292","doi":"10.1038/s41597-020-00712-x","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":"Scientific Data","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":223,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Ministry of Education, India; National Research Foundation","keywords":"ASHRAE 90.1; Benchmarking; Data set; Range (aeronautics); Energy (signal processing); Competition (biology); Metre; Measure (data warehouse)","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":[0.002216436,0.001347487,0.001021423,0.002759204,0.0008114552,0.001777784,0.001725398,0.001687313,0.01031123],"category_scores_gemma":[0.008970054,0.0004573597,0.0009074597,0.006814499,0.0004286232,0.0009015081,0.001976437,0.001679729,0.01306041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001020355,"about_ca_system_score_gemma":0.002578948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03273484,"about_ca_topic_score_gemma":0.05466685,"domain_scores_codex":[0.9976132,0.0004673291,0.0001394267,0.0006183333,0.0009186341,0.0002431979],"domain_scores_gemma":[0.9962333,0.0009155358,0.0003300155,0.0007799336,0.001318804,0.0004223397],"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":[0.0004536909,0.0002475469,0.02392676,0.0009257784,0.0001957873,0.0001614585,0.0001445374,0.005192666,0.00178929,0.002350323,0.940915,0.02369714],"study_design_scores_gemma":[0.0004187262,0.0001902478,0.09207874,0.0003326357,0.0001120895,0.0002664194,0.0004045693,0.01094065,0.003539237,0.003918964,0.8876792,0.0001186247],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.005808775,0.000214546,0.001200131,0.0002469339,0.00007840464,0.0000357936,0.9891956,0.001185849,0.002034043],"genre_scores_gemma":[0.003491895,0.00004593514,0.001673079,0.00004804244,0.000007653616,0.00004851778,0.9940956,0.0001130853,0.0004761568],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03273484,"threshold_uncertainty_score":0.06508863,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07491076497867988,"score_gpt":0.2493241337972274,"score_spread":0.1744133688185475,"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."}}