{"id":"W4230746207","doi":"10.32920/ryerson.14649519","title":"Investigating building information model to building energy model data transfer integrity and simulation results","year":2021,"lang":"en","type":"preprint","venue":"","topic":"BIM and Construction Integration","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Sciencetech (Canada); University of Waterloo","funders":"Lawrence Berkeley National Laboratory; Construction Industry Council; U.S. General Services Administration; U.S. Department of Energy","keywords":"Building information modeling; Reliability (semiconductor); Building energy simulation; Energy modeling; Computer science; Process (computing); Energy (signal processing); Reliability engineering; Efficient energy use; Energy performance; Simulation; Engineering; Operating system","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":[0.006858784,0.0008580458,0.0007641092,0.001311764,0.0006224007,0.001949886,0.001387836,0.0007257671,0.00463685],"category_scores_gemma":[0.03279445,0.0005863757,0.0008541462,0.001504724,0.0007138178,0.00218577,0.001507532,0.001164619,0.001323506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001416847,"about_ca_system_score_gemma":0.001329083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0125967,"about_ca_topic_score_gemma":0.005350877,"domain_scores_codex":[0.9952986,0.001449862,0.0003522016,0.0005396179,0.002084515,0.0002753317],"domain_scores_gemma":[0.9787346,0.009500478,0.0008343412,0.005405168,0.00528119,0.0002443067],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001817762,0.0009842898,0.06037707,0.0004018037,0.0002309369,0.0003006272,0.001091952,0.8105986,0.02042067,0.009107496,0.003210499,0.09145828],"study_design_scores_gemma":[0.0000558919,0.0002644076,0.01379794,0.00004574035,0.00005939406,0.00007341402,0.0003759868,0.93163,0.04890029,0.001225976,0.003535326,0.00003570653],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8806947,0.0001786505,0.09690685,0.0003583701,0.00006999786,0.0001898666,0.002391596,0.006441622,0.01276825],"genre_scores_gemma":[0.967809,0.00007809546,0.02715672,0.00003415032,0.000006715048,0.00007743526,0.003041095,0.0007618535,0.001035042],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0125967,"threshold_uncertainty_score":0.03627312,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05871535917630574,"score_gpt":0.2794111299277532,"score_spread":0.2206957707514475,"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."}}