{"id":"W3100141898","doi":"10.1155/2020/8840200","title":"Risk Assessment System Based on Fuzzy Composite Evaluation and a Backpropagation Neural Network for a Shield Tunnel Crossing under a River","year":2020,"lang":"en","type":"article","venue":"Advances in Civil Engineering","topic":"Geotechnical Engineering and Analysis","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Transportation of Ontario","funders":"","keywords":"Shield; Backpropagation; Risk assessment; Artificial neural network; Computer simulation; Environmental science; Civil engineering; Engineering; Computer science; Simulation; Geology; 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":[0.00202565,0.001373047,0.0008294237,0.001675969,0.0008310458,0.001314912,0.000914111,0.001102701,0.001153589],"category_scores_gemma":[0.002825421,0.0004570115,0.001023102,0.000534525,0.0004804321,0.00161567,0.001108757,0.0005504075,0.0001400365],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001698074,"about_ca_system_score_gemma":0.001303998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01266176,"about_ca_topic_score_gemma":0.00667199,"domain_scores_codex":[0.9986776,0.0002809667,0.0001336766,0.0003024226,0.0004856035,0.0001197242],"domain_scores_gemma":[0.99913,0.0002233306,0.0001160836,0.0000360719,0.0004421833,0.00005232038],"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":[0.00043495,0.0002103555,0.01233652,0.0001280959,0.0001945901,0.0003696746,0.0002670823,0.8596217,0.007866207,0.002678519,0.0009026769,0.1149896],"study_design_scores_gemma":[0.00001012339,0.00006210861,0.001268967,0.000007050366,0.00003158541,0.00002749227,0.0000227715,0.9967301,0.001084727,0.0006169002,0.0001234823,0.00001470984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.236311,0.0003209675,0.7589446,0.0002881965,0.00004068742,0.000204305,0.0000792319,0.0006676549,0.003143367],"genre_scores_gemma":[0.9616174,0.0001481808,0.03643617,0.00003675224,0.00001685313,0.0001387208,0.00007694633,0.00001267263,0.001516249],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01266176,"threshold_uncertainty_score":0.02517611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01076631691598877,"score_gpt":0.2489460248926781,"score_spread":0.2381797079766893,"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."}}