{"id":"W2506450184","doi":"10.1016/j.aap.2016.07.028","title":"Macro-level safety analysis of pedestrian crashes in Shanghai, China","year":2016,"lang":"en","type":"article","venue":"Accident Analysis & Prevention","topic":"Traffic and Road Safety","field":"Engineering","cited_by":126,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"Fundamental Research Funds for the Central Universities; Science and Technology Commission of Shanghai Municipality; National Natural Science Foundation of China","keywords":"Pedestrian; Intersection (aeronautics); Centroid; Transport engineering; Crash; Adjacency list; Poison control; Statistics; Geography; Computer science; Mathematics; Engineering; Algorithm; Artificial intelligence; Environmental health","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005581183,0.0001970166,0.0006281048,0.001396375,0.00004531987,0.00001776496,0.0002487411,0.0001184508,0.001589367],"category_scores_gemma":[0.00003101914,0.0001528021,0.0008403524,0.003621956,0.00002512343,0.0002678284,0.00004773511,0.00008512088,0.00002289409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001350524,"about_ca_system_score_gemma":0.0000151593,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003130556,"about_ca_topic_score_gemma":0.03199102,"domain_scores_codex":[0.9982718,0.00009901346,0.0007587682,0.000293433,0.0002849766,0.0002920686],"domain_scores_gemma":[0.9992502,0.00006866948,0.0001522578,0.0004154028,0.00003971758,0.00007378719],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002432517,0.00007221716,0.8899311,0.000004581546,0.00679808,0.000003250095,0.0001857921,0.04003832,0.0007973911,0.00008661951,0.00004056902,0.06201771],"study_design_scores_gemma":[0.0004896254,0.00001401152,0.9789443,0.00002871198,0.006349535,3.046141e-7,0.00004292535,0.0128702,0.0007066274,0.0002946989,0.00006780373,0.0001912504],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8807266,0.0003040257,0.1180638,0.00008486767,0.00006628952,0.0001160665,0.000006937417,0.00009778352,0.000533564],"genre_scores_gemma":[0.9982315,0.0007109498,0.0003769962,0.000003664543,0.00003169964,0.0000125184,0.0001329146,0.00001748158,0.0004822288],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1176868,"threshold_uncertainty_score":0.9993233,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01337104698884556,"score_gpt":0.2453929028608372,"score_spread":0.2320218558719917,"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."}}