{"id":"W2990810198","doi":"10.1061/(asce)co.1943-7862.0001747","title":"Dynamic and Proactive Risk-Based Methodology for Managing Excessive Geometric Variability Issues in Modular Construction Projects Using Bayesian Theory","year":2019,"lang":"en","type":"article","venue":"Journal of Construction Engineering and Management","topic":"BIM and Construction Integration","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Modular programming; Risk analysis (engineering); Modular design; Risk management; Schedule; Process (computing); Computer science; Risk assessment; Systems engineering; Engineering","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":[0.0009561323,0.0001661548,0.0002947699,0.0009617794,0.00004587391,0.00004387145,0.00005059585,0.00009039704,0.000007141633],"category_scores_gemma":[0.00006993546,0.0001636018,0.00005488135,0.0003379379,0.00005938437,0.0002847483,0.0000143257,0.0002210547,1.504107e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001540403,"about_ca_system_score_gemma":0.00001748273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005361774,"about_ca_topic_score_gemma":0.000001328477,"domain_scores_codex":[0.9990366,0.0001021205,0.0004004742,0.0001794845,0.0001155723,0.0001657491],"domain_scores_gemma":[0.9994032,0.0001698878,0.0001907369,0.00009986802,0.00008931325,0.00004698718],"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":[0.0002088471,0.00002537741,0.006120878,0.001268161,0.0005330447,0.000007417219,0.0003254405,0.6411986,0.002651259,0.03378,0.000004192524,0.3138767],"study_design_scores_gemma":[0.001580244,0.0001125498,0.007096193,0.0001927288,0.0002093442,0.0003168479,0.001755922,0.9780499,0.001169002,0.009004779,0.0002450818,0.0002673939],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3837313,0.0003281914,0.6147944,0.00002137393,0.0007051855,0.000318986,0.000002851291,0.00003271583,0.00006501576],"genre_scores_gemma":[0.7311202,0.0003425426,0.2684606,0.000004339636,0.00003300586,0.00001587692,0.000001233298,0.00001607501,0.000006133537],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3473889,"threshold_uncertainty_score":0.6671488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009643938416521142,"score_gpt":0.2384410774333077,"score_spread":0.2287971390167865,"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."}}