{"id":"W2137355359","doi":"10.3141/2393-09","title":"Application of Computer Vision to Diagnosis of Pedestrian Safety Issues","year":2013,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Pedestrian; Intersection (aeronautics); Computer science; Transport engineering; Collision; Computer security; Risk analysis (engineering); Engineering; Business","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0006751145,0.000501734,0.0003474766,0.003688224,0.0003187484,0.0008717037,0.0004232615,0.0006903457,0.0008828692],"category_scores_gemma":[0.002194813,0.0002866165,0.0003032855,0.001076034,0.000437324,0.0004886786,0.0005034975,0.0004948605,0.0003430404],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005267434,"about_ca_system_score_gemma":0.0008249807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007387466,"about_ca_topic_score_gemma":0.006683781,"domain_scores_codex":[0.9993686,0.0001868678,0.00003140235,0.0001124921,0.0002278567,0.00007272037],"domain_scores_gemma":[0.9989836,0.0003888143,0.0001113699,0.00008054187,0.0003979364,0.00003767175],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002690784,0.0003691331,0.01855437,0.0002575768,0.00009400725,0.0005000972,0.0003502762,0.05775466,0.1025358,0.003101701,0.00354298,0.8126702],"study_design_scores_gemma":[0.00004636178,0.0004209311,0.04525344,0.00007621687,0.00007235266,0.001050253,0.0004396762,0.8675733,0.0703202,0.00779764,0.00687466,0.00007501978],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.237069,0.001485255,0.7476979,0.0004206993,0.0001477543,0.0002304038,0.0002546456,0.002151425,0.01054292],"genre_scores_gemma":[0.7518704,0.0007398974,0.2453752,0.0001314695,0.0000589196,0.00004880257,0.0001933717,0.00003531848,0.001546645],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007387466,"threshold_uncertainty_score":0.01468891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03082890627923999,"score_gpt":0.3406954679642102,"score_spread":0.3098665616849702,"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."}}