{"id":"W4398568004","doi":"10.7910/dvn/n3hgrn","title":"HUE: The Hourly Usage of Energy Dataset for Buildings in British Columbia","year":2018,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Hue; Geography; Architectural engineering; Cartography; Meteorology; Computer science; Artificial intelligence; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002893708,0.0001898217,0.0003383397,0.00008611483,0.00009408183,0.0002073582,0.0008726711,0.0002961584,0.002223716],"category_scores_gemma":[0.0000763641,0.0002618724,0.00007193301,0.0002346909,0.0001099549,0.0002498291,0.0001953898,0.0002043698,0.00007010621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005782074,"about_ca_system_score_gemma":0.00004528277,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02696091,"about_ca_topic_score_gemma":0.2053727,"domain_scores_codex":[0.9987424,0.00003394327,0.0004012086,0.0003179575,0.0001981211,0.0003063544],"domain_scores_gemma":[0.9986637,0.00009733698,0.0001306415,0.001009461,0.00004526576,0.00005363751],"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":[0.000008474633,0.00002994109,0.00001086422,0.000142405,0.00005896116,0.00001042921,0.00000328448,0.009544032,0.000005954262,0.000008595162,0.9897885,0.0003885037],"study_design_scores_gemma":[0.0003982928,0.0000353148,0.00002854629,0.0001766675,0.00007608048,0.00001327769,0.000007195855,0.00359336,0.00002402545,0.00004127154,0.9953289,0.0002770535],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001889072,0.000006001522,0.001828101,0.000001110599,0.0005681505,0.0001821016,0.9971694,0.0000419591,0.00001431845],"genre_scores_gemma":[0.0001038207,0.0008077536,0.001105017,0.0001396505,0.0002049886,0.00009673154,0.9974134,0.0000437212,0.00008492186],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1784118,"threshold_uncertainty_score":0.9999834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007831671169447848,"score_gpt":0.2001831378892819,"score_spread":0.1923514667198341,"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."}}