{"id":"W2974503562","doi":"10.1061/ajrua6.0001022","title":"Utilizing Partial Least-Squares Path Modeling to Analyze Crash Risk Contributing Factors for Shanghai Urban Expressway System","year":2019,"lang":"en","type":"article","venue":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part A Civil Engineering","topic":"Traffic and Road Safety","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Education and Child Care","funders":"","keywords":"Partial least squares regression; Crash; Transport engineering; Path (computing); Computer science; Engineering; Statistics; Mathematics","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":[0.001631815,0.001255433,0.0004034177,0.002381012,0.0004798963,0.0008129757,0.0005459681,0.0003419899,0.00204179],"category_scores_gemma":[0.003663287,0.0004057283,0.001410242,0.001811317,0.0002925787,0.000912682,0.0008093964,0.0005851039,0.0001771496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001003477,"about_ca_system_score_gemma":0.003081397,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05080237,"about_ca_topic_score_gemma":0.03455075,"domain_scores_codex":[0.9993524,0.0003091242,0.00003128326,0.0001321817,0.00009308309,0.00008198025],"domain_scores_gemma":[0.9987213,0.0007680865,0.0001442169,0.00005307426,0.0002637993,0.00004945292],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001717218,0.0002117793,0.3320994,0.0002323723,0.0003927487,0.0004854834,0.0008697832,0.5834761,0.001613953,0.005844437,0.001519537,0.0730826],"study_design_scores_gemma":[0.00001216823,0.0001138974,0.05146599,0.00001442967,0.00009090459,0.00003549992,0.0003665214,0.9451602,0.0002947658,0.001908821,0.0005073465,0.00002946687],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9213261,0.0001249597,0.07638098,0.0001505192,0.00001510339,0.0001745256,0.0008719961,0.0002400155,0.0007156101],"genre_scores_gemma":[0.979686,0.0001373199,0.01794549,0.00001194938,0.000006486647,0.000160563,0.001380238,0.00002396646,0.0006480881],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05080237,"threshold_uncertainty_score":0.1010134,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007002765672670204,"score_gpt":0.1967679329980034,"score_spread":0.1897651673253332,"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."}}