{"id":"W3005434831","doi":"10.36680/j.itcon.2020.005","title":"Risk quantification using fuzzy-based Monte Carlo simulation","year":2020,"lang":"en","type":"article","venue":"Journal of Information Technology in Construction","topic":"Construction Project Management and Performance","field":"Decision Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Monte Carlo method; Cost estimate; Cost contingency; Computer science; Variance (accounting); Contingency; Contingency table; Fuzzy logic; Covariance matrix; Estimation; Data mining; Econometrics; Operations research; Reliability engineering; Statistics; Mathematical optimization; Algorithm; Mathematics; Engineering; Machine learning; Artificial intelligence","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":[0.003419628,0.0006967746,0.001193219,0.002169579,0.000713271,0.001595813,0.0012092,0.00135135,0.002497096],"category_scores_gemma":[0.008264763,0.0004743381,0.001128211,0.001375208,0.000769576,0.001125476,0.0008891683,0.0009555125,0.0001612592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001735004,"about_ca_system_score_gemma":0.001382773,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01327079,"about_ca_topic_score_gemma":0.008138273,"domain_scores_codex":[0.9985024,0.0007157678,0.00009411224,0.0001371118,0.0004553602,0.00009525039],"domain_scores_gemma":[0.9922993,0.006245697,0.0004127463,0.0002632835,0.0006894966,0.00008957317],"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":[0.00001312398,0.000009195174,0.000264325,0.00001446364,0.0000134349,0.00001677197,0.00001509683,0.9914392,0.0001423074,0.004420228,0.00005049934,0.003601379],"study_design_scores_gemma":[0.000001268148,0.000003449607,0.00004505372,0.000003067149,0.000002097416,0.000004615745,0.000002875119,0.998706,0.00007185413,0.001102739,0.0000538991,0.000003088002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02677747,0.0001555798,0.9700657,0.00007536369,0.00001552207,0.00009452679,0.0000614021,0.0001849162,0.002569507],"genre_scores_gemma":[0.754621,0.0002880098,0.2431137,0.00003882892,0.00002469491,0.0003495063,0.000117037,0.00003825746,0.001408985],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01327079,"threshold_uncertainty_score":0.0263871,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07772291270651605,"score_gpt":0.3542710614909876,"score_spread":0.2765481487844715,"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."}}