{"id":"W2900286742","doi":"10.1049/iet-its.2018.5160","title":"Predicting drivers' direction sign reading reaction time using an integrated cognitive architecture","year":2018,"lang":"en","type":"article","venue":"IET Intelligent Transport Systems","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Architecture; Sign (mathematics); Cognitive architecture; Reading (process); Computer science; Cognition; Artificial intelligence; Psychology; Mathematics; Art; Linguistics; Neuroscience; Visual arts","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006070975,0.0003196083,0.0003643328,0.0003992958,0.0003920305,0.00006387817,0.0001557727,0.0002965139,0.002134285],"category_scores_gemma":[0.00003510894,0.0003092139,0.0001535881,0.0004012823,0.0001296993,0.0003609241,0.000006145311,0.0004574638,0.0009926263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002540638,"about_ca_system_score_gemma":0.00005025553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001868198,"about_ca_topic_score_gemma":0.0002687138,"domain_scores_codex":[0.9974329,0.0004123228,0.0008112323,0.0006184829,0.0003331886,0.0003918062],"domain_scores_gemma":[0.9985461,0.000138489,0.0003587154,0.0003032782,0.0004670265,0.0001864072],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.007207385,0.004309074,0.08849449,0.0006251587,0.005117479,0.000474234,0.4683425,0.008349675,0.2773775,0.004297346,0.004775271,0.13063],"study_design_scores_gemma":[0.006409514,0.006661734,0.1098458,0.008297597,0.002346365,0.00397023,0.210937,0.2953204,0.03767725,0.0003045679,0.3122469,0.005982696],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7623733,0.00002919591,0.202214,0.00002274249,0.006369118,0.0008361449,0.00009345925,0.0008533624,0.02720868],"genre_scores_gemma":[0.9930235,0.000004632012,0.00009974514,0.00006360655,0.00112625,0.00005312139,0.0003382818,0.00006250389,0.005228356],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3074716,"threshold_uncertainty_score":0.999936,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04113923474345275,"score_gpt":0.3355800540910097,"score_spread":0.2944408193475569,"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."}}