{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001766251,0.0007021722,0.001368976,0.0007347925,0.0001421528,0.0001765645,0.0003790204,0.0003098061,0.000004795976],"category_scores_gemma":[0.0004709134,0.0006541742,0.0003637533,0.0004992267,0.00001244274,0.0003965199,0.00007424751,0.0009190963,0.000004610061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004650324,"about_ca_system_score_gemma":0.00005129839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001579707,"about_ca_topic_score_gemma":0.00004093738,"domain_scores_codex":[0.9961743,0.00007658597,0.001634193,0.0004475953,0.0005006923,0.00116663],"domain_scores_gemma":[0.9978403,0.0007617616,0.0003308733,0.0003785799,0.0002117518,0.0004766805],"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":[0.00005077852,0.00001934093,0.02250088,0.001145266,0.0002618901,0.00001619791,0.001457744,0.9730774,0.0005988753,0.0005659074,0.0000668926,0.0002388037],"study_design_scores_gemma":[0.001581766,0.0001201056,0.003253125,0.002924559,0.0001549435,0.00005177629,0.00220556,0.9867219,0.0001101256,0.000001728358,0.002158641,0.0007157151],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7035325,0.003502931,0.2890502,0.000004598854,0.002828435,0.0005712416,0.0001477393,0.0003332463,0.0000291],"genre_scores_gemma":[0.9979123,0.0003203125,0.0007477114,0.000002043553,0.0007792851,0.00005511281,0.00001372265,0.0001600639,0.000009444077],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2943798,"threshold_uncertainty_score":0.9995909,"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."}}