{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005941673,0.0002326241,0.0002493787,0.0004556065,0.0006000482,0.0005471421,0.0005713759,0.00007087897,0.00137761],"category_scores_gemma":[0.00007643189,0.0002454541,0.0001078895,0.0003103969,0.00005401336,0.0007282637,0.0001999034,0.000356822,0.00007352702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001755694,"about_ca_system_score_gemma":0.0001521976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001126846,"about_ca_topic_score_gemma":0.000006031366,"domain_scores_codex":[0.9974163,0.0002914224,0.0005282775,0.000590558,0.0009106555,0.0002627775],"domain_scores_gemma":[0.9986089,0.0001602498,0.0003456653,0.0002869251,0.0004668516,0.0001314485],"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":[0.0002822324,0.001186422,0.0003021484,0.00005073956,0.0003371905,0.00007397443,0.001458799,0.1338574,0.007429343,0.3211772,0.005777002,0.5280675],"study_design_scores_gemma":[0.001409727,0.0003637616,0.00181177,0.00004496277,0.00002512306,0.00005096466,0.0001317497,0.9834653,0.00008351682,0.01086203,0.001452183,0.0002988734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2105889,0.0001197503,0.7610299,0.0157976,0.004004311,0.001106416,0.0002660168,0.0007314131,0.006355771],"genre_scores_gemma":[0.9940141,0.00006704954,0.003997349,0.001240949,0.0001610385,0.00006016063,0.0001685543,0.00001864463,0.0002720924],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8496079,"threshold_uncertainty_score":0.9999998,"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."}}