{"id":"W4367186203","doi":"10.1016/j.trip.2023.100822","title":"How do drivers allocate visual attention to vulnerable road users when turning at urban intersections?","year":2023,"lang":"en","type":"article","venue":"Transportation Research Interdisciplinary Perspectives","topic":"Traffic and Road Safety","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Gaze; Pedestrian; Transport engineering; Eye tracking; Computer science; Geography; Engineering; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006227807,0.0002631952,0.0002420697,0.0009679295,0.0008828315,0.0001676903,0.0002849757,0.0001242497,0.0002554328],"category_scores_gemma":[0.00002727452,0.0002656494,0.0001844129,0.001135318,0.000183715,0.0006106077,0.0000876833,0.0007122122,0.0004217245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006991999,"about_ca_system_score_gemma":0.00003019261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005148827,"about_ca_topic_score_gemma":0.0009681583,"domain_scores_codex":[0.9976292,0.0001495436,0.0002877834,0.0005850065,0.0006326018,0.0007158176],"domain_scores_gemma":[0.9990717,0.0001092878,0.00003180598,0.00025434,0.0002501016,0.0002828174],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.000902243,0.0003009539,0.02412772,0.0003845953,0.000816292,0.0002957983,0.552411,0.2504618,0.05214477,0.001693529,0.1080447,0.008416611],"study_design_scores_gemma":[0.0009279379,0.0005424865,0.6035434,0.0002645923,0.00003693177,0.000005229989,0.3608846,0.02828079,0.001038393,0.000320701,0.003540801,0.0006141278],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9893238,0.0001212088,0.003742145,0.002861507,0.0004773714,0.0005307178,0.00005256051,0.001045461,0.001845205],"genre_scores_gemma":[0.9915012,0.00008599626,0.0002103487,0.000006288486,0.0001759025,0.0001585499,0.0001631482,0.00007537181,0.007623254],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5794157,"threshold_uncertainty_score":0.9999796,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03163968812257727,"score_gpt":0.3361393735073084,"score_spread":0.3044996853847312,"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."}}