{"id":"W2055922601","doi":"10.1002/atr.5670420104","title":"Development of pictograms for dynamic traffic control systems in South Korea","year":2008,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Safety Warnings and Signage","field":"Psychology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Pictogram; Salient; Context (archaeology); Comprehension; Reading (process); Set (abstract data type); Exploratory research; Control (management); Computer science; Usability; Transport engineering; Engineering; Human–computer interaction; Geography; Artificial intelligence; Linguistics; Sociology","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":[0.0002508367,0.00009735476,0.0003276786,0.0001693442,0.00003752078,0.000002364529,0.00008189637,0.00006634837,0.00001222341],"category_scores_gemma":[0.00001024413,0.00008683372,0.0001108549,0.0001308477,0.00002529719,0.000101004,2.715477e-7,0.0001184194,0.000001157702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003990609,"about_ca_system_score_gemma":0.00006542145,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003636387,"about_ca_topic_score_gemma":0.00005176749,"domain_scores_codex":[0.9986495,0.00002483506,0.0008981191,0.000103212,0.0001691225,0.0001552423],"domain_scores_gemma":[0.9989867,0.00007307193,0.0006856567,0.00006297486,0.0001464544,0.00004517052],"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":[0.005070002,0.0007104184,0.01734604,0.0002478935,0.0004228008,0.0001852637,0.2792203,0.6054646,0.0284227,0.0006664445,0.00001697553,0.06222655],"study_design_scores_gemma":[0.009709693,0.0004309632,0.9789628,0.0001774974,0.00006725167,0.00003134151,0.008486379,0.0004636275,0.0001640768,0.0000321972,0.00131058,0.0001635627],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9456638,0.0005354039,0.05299042,0.00001655876,0.0004592038,0.0002929127,0.00001493333,0.000007727508,0.00001908071],"genre_scores_gemma":[0.9930668,0.00001415497,0.006791452,0.0000110186,0.00002778256,0.00002242454,0.0000212849,0.00001531784,0.00002973603],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9616168,"threshold_uncertainty_score":0.3540976,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01576546404040555,"score_gpt":0.2748522872326492,"score_spread":0.2590868231922436,"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."}}