{"id":"W3044999828","doi":"10.1139/cjce-2020-0032","title":"Hybrid fuzzy system dynamics model for analyzing the impacts of interrelated risk and opportunity events on project contingency","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Construction Project Management and Performance","field":"Decision Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Fuzzy logic; Risk analysis (engineering); Computer science; Risk assessment; Contingency; Judgement; Work (physics); Operations research; Management science; Engineering; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.000723071,0.0006764792,0.0004950074,0.0008693782,0.0005051161,0.001128785,0.0008143228,0.0008676738,0.003210126],"category_scores_gemma":[0.001303499,0.0003192234,0.0007963997,0.0005038118,0.0004051759,0.001014682,0.0005679998,0.0008227211,0.0003297702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001235148,"about_ca_system_score_gemma":0.001451785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01516254,"about_ca_topic_score_gemma":0.0108775,"domain_scores_codex":[0.9996814,0.0001021521,0.00001526999,0.00007218763,0.00009959699,0.0000294793],"domain_scores_gemma":[0.9996123,0.0002423117,0.00004215503,0.00001553844,0.00007155448,0.00001621713],"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":[0.0000255456,0.00002096874,0.0005203329,0.00002339154,0.00002986048,0.00004333803,0.00005786332,0.9790765,0.001188455,0.01128015,0.0002459867,0.007487577],"study_design_scores_gemma":[0.000002821486,0.000007941207,0.00007881825,0.000001967565,0.000003809793,0.000004130095,0.000005758028,0.9981318,0.00007513384,0.001482637,0.0002024644,0.000002714339],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02570854,0.00009378198,0.9675628,0.0001397266,0.00002632556,0.00005831163,0.0001659013,0.0001769388,0.006067682],"genre_scores_gemma":[0.8782103,0.0002655832,0.1129585,0.00007367078,0.00002795724,0.0004355637,0.000248907,0.00003419511,0.007745216],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01516254,"threshold_uncertainty_score":0.03014857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05449068122408518,"score_gpt":0.2801892848344276,"score_spread":0.2256986036103424,"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."}}