{"id":"W1978353383","doi":"10.1016/s0001-4575(01)00043-4","title":"Pedestrian risk decrease with pedestrian flow. A case study based on data from signalized intersections in Hamilton, Ontario","year":2002,"lang":"en","type":"article","venue":"Accident Analysis & Prevention","topic":"Traffic and Road Safety","field":"Engineering","cited_by":149,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Luleå Tekniska Universitet; University of Toronto; Connaught Fund; Ford Foundation","keywords":"Pedestrian; Intersection (aeronautics); Transport engineering; Channelized; Traffic flow (computer networking); Pedestrian crossing; Poison control; Engineering; Simulation; Computer science; Telecommunications; Computer security; Medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004272494,0.0002919915,0.000441481,0.0006647373,0.0001614946,0.0001080279,0.0003308401,0.00009311352,0.002743021],"category_scores_gemma":[0.00004538383,0.0002663658,0.0002178766,0.001197777,0.00001640372,0.0003732733,0.00004627034,0.0003792656,0.00005651539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003191725,"about_ca_system_score_gemma":0.00004473501,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.209672,"about_ca_topic_score_gemma":0.9847659,"domain_scores_codex":[0.9979443,0.0003040942,0.0005741318,0.0005878788,0.0003273753,0.0002622068],"domain_scores_gemma":[0.9984121,0.0001294631,0.0001391253,0.001109103,0.00003492753,0.0001752856],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001199082,0.0007678526,0.6980819,0.000001358611,0.001243355,0.0006633998,0.001671503,0.2948546,0.000001924247,1.291109e-7,0.000166857,0.002427261],"study_design_scores_gemma":[0.002987186,0.0001894389,0.3653105,0.00004344782,0.003870518,0.00001453761,0.001688709,0.6254888,0.000004007275,0.000008669205,0.0001084014,0.0002857698],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.951063,0.00008717879,0.04779115,0.0000133952,0.00009302655,0.0005275951,0.000009878934,0.0001826532,0.0002321163],"genre_scores_gemma":[0.9982218,0.00003722342,0.0007614617,0.00001009117,0.00005630944,0.00005357094,0.0006094419,0.0000313414,0.0002187333],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7750939,"threshold_uncertainty_score":0.9999788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03656913715013043,"score_gpt":0.2561894794822859,"score_spread":0.2196203423321554,"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."}}