{"id":"W4383682054","doi":"10.35490/ec3.2023.320","title":"Future research directions of construction digital twins","year":2023,"lang":"en","type":"article","venue":"Computing in construction","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Automation; Computer science; Enhanced Data Rates for GSM Evolution; Order (exchange); Construction industry; Data science; Engineering; Construction engineering; Telecommunications; Business","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000278562,0.0001012944,0.0001397842,0.0007129285,0.00009606342,0.00008068377,0.0001044745,0.0001401421,0.00001709468],"category_scores_gemma":[0.00005475534,0.0001206386,0.00004493052,0.002094925,0.0002289849,0.0004415417,0.00003052058,0.0004178885,0.00006649035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000112862,"about_ca_system_score_gemma":0.00003332868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004971437,"about_ca_topic_score_gemma":0.000003116954,"domain_scores_codex":[0.99892,0.00002877447,0.0003834628,0.0001457036,0.0002543328,0.0002677029],"domain_scores_gemma":[0.9994901,0.0001534962,0.00003570558,0.0001549165,0.0001214838,0.00004422916],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009568173,0.00002655959,0.04405552,0.0002528466,0.00004311176,0.000005439556,0.001016507,0.05558162,0.0006643485,0.01624063,0.001465351,0.8806385],"study_design_scores_gemma":[0.004898055,0.0003152565,0.2048416,0.00284118,0.00004713457,0.001544623,0.07923814,0.4597265,0.02725109,0.03151438,0.1852209,0.002561227],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9239347,0.00006555707,0.002503108,0.00007366344,0.003643433,0.0001889379,0.00003240218,0.0009096161,0.06864857],"genre_scores_gemma":[0.9983886,0.000077934,0.001144435,0.000001272428,0.000302349,0.000005902184,0.00003007476,0.00002026938,0.00002918892],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8780773,"threshold_uncertainty_score":0.4919501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03622556853986306,"score_gpt":0.2899873782045952,"score_spread":0.2537618096647322,"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."}}