{"id":"W2520420456","doi":"10.1002/atr.1397","title":"Developing evasive action‐based indicators for identifying pedestrian conflicts in less organized traffic environments","year":2016,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic and Road Safety","field":"Engineering","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Pedestrian; Intersection (aeronautics); Traffic conflict; Computer science; Collision; Transport engineering; Traffic congestion; Computer security; Engineering; Floating car data","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001391926,0.00012036,0.0002075009,0.0002740879,0.00004159866,0.000006958149,0.00008708525,0.00007521248,0.000009843278],"category_scores_gemma":[0.00001963336,0.00009683093,0.00007712338,0.0001894936,0.00001824381,0.0003901766,5.404531e-7,0.000103837,0.000001725454],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002136912,"about_ca_system_score_gemma":0.00006079639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":4.794589e-7,"about_ca_topic_score_gemma":0.00006120913,"domain_scores_codex":[0.999024,0.00001348957,0.0005215249,0.00009778226,0.0001785382,0.0001647069],"domain_scores_gemma":[0.9995428,0.00009818399,0.0002112642,0.00005572545,0.00003007376,0.00006194443],"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":[0.0004556203,0.00006499251,0.005095794,0.0002359674,0.0001015274,0.00003104227,0.002999049,0.805397,0.06569297,0.00009959804,0.00001196691,0.1198144],"study_design_scores_gemma":[0.01292576,0.0001253284,0.9127874,0.0007127054,0.00009664748,0.000007989417,0.001666325,0.0004115801,0.06706671,0.0001175579,0.003684754,0.0003972471],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7824894,0.00008500852,0.2167533,0.000106628,0.0003719384,0.0001575553,0.000008375779,0.00002519721,0.000002614405],"genre_scores_gemma":[0.9900551,0.0003214502,0.009499711,0.00001268798,0.00005063983,0.000008938052,0.0000134016,0.00002912114,0.00000895631],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9076916,"threshold_uncertainty_score":0.3948651,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03147386045140065,"score_gpt":0.2753217435494488,"score_spread":0.2438478830980482,"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."}}