{"id":"W2886832304","doi":"10.5539/mas.v12n9p87","title":"Determining Confusion for Traditional and Experimental Pedestrian Signals in Rural and Suburban Areas in the United States","year":2018,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Safety Warnings and Signage","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pedestrian; SIGNAL (programming language); Comprehension; Computer science; Confusion; Computer security; Simulation; Transport engineering; Psychology; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006714473,0.00008676646,0.00009463172,0.0001643427,0.0002258666,0.00007335855,0.0001685172,0.00003451708,0.00004144126],"category_scores_gemma":[0.00001307437,0.00006653502,0.000008790413,0.000247073,0.000648653,0.00007395631,0.0000256335,0.0000750936,0.000001824897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001941936,"about_ca_system_score_gemma":0.00002196832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001167766,"about_ca_topic_score_gemma":0.00003406152,"domain_scores_codex":[0.9991618,0.00003003254,0.0001415627,0.0002653234,0.0001514549,0.0002498099],"domain_scores_gemma":[0.9996012,0.0001976896,0.00004416826,0.00009421948,0.00001524628,0.00004744848],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0005039729,0.0002629315,0.01375143,0.000009910703,0.000005373592,0.00001442219,0.1617658,0.00005935136,0.7921804,0.01690666,0.00012764,0.01441209],"study_design_scores_gemma":[0.008337009,0.001336036,0.4768683,0.00009526701,0.00001368184,0.0001068483,0.07787409,0.3688397,0.01972817,0.04531151,0.0007165574,0.0007727771],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952226,0.00004882198,0.002488147,0.0001187714,0.00004317818,0.0003239242,0.000007443131,0.000009896406,0.001737272],"genre_scores_gemma":[0.9992269,0.000001818558,0.0001770036,0.0004379014,0.00003563089,0.00008681107,0.00001207876,0.000005591659,0.00001621387],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7724522,"threshold_uncertainty_score":0.271322,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06858707588768989,"score_gpt":0.3179655143302051,"score_spread":0.2493784384425152,"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."}}