{"id":"W4315488937","doi":"10.1109/icarcv57592.2022.10004297","title":"Traffic Sign Recognition Using Ulam's Game","year":2022,"lang":"en","type":"article","venue":"2022 17th International Conference on Control, Automation, Robotics and Vision (ICARCV)","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Pooling; Computer science; Classifier (UML); Feature extraction; Artificial intelligence; Sign (mathematics); Pattern recognition (psychology); Traffic sign; Feature (linguistics); Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001016657,0.00111712,0.001240482,0.001129736,0.0005805101,0.001125265,0.002497134,0.0007568658,0.003543342],"category_scores_gemma":[0.001871917,0.0003969924,0.0008691757,0.0004476755,0.0006084928,0.001843048,0.001737261,0.001001958,0.001004414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007756224,"about_ca_system_score_gemma":0.0009054379,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007583597,"about_ca_topic_score_gemma":0.007645086,"domain_scores_codex":[0.9992757,0.0001305416,0.00005329921,0.0002035342,0.0001964436,0.0001405665],"domain_scores_gemma":[0.9995925,0.0001043023,0.00003360934,0.00005899164,0.0001426129,0.00006803872],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00116486,0.0008554313,0.007353891,0.000154772,0.0003197062,0.0006449487,0.0002241843,0.1284508,0.04097158,0.02331128,0.008429987,0.7881187],"study_design_scores_gemma":[0.00001491089,0.0001088454,0.0005018993,0.000006860955,0.00002377204,0.00007067289,0.00001213188,0.9906011,0.004702633,0.002582651,0.001356683,0.00001779182],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04200649,0.0001801222,0.9450191,0.0001822232,0.0002002584,0.000256858,0.0001380771,0.006433459,0.005583413],"genre_scores_gemma":[0.6913384,0.000129534,0.3005147,0.0004193037,0.00009016957,0.0003327519,0.0003871646,0.0001589674,0.00662901],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007583597,"threshold_uncertainty_score":0.0150789,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04804392962726899,"score_gpt":0.3090697158312911,"score_spread":0.2610257862040221,"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."}}