{"id":"W4386345866","doi":"10.21203/rs.3.rs-3299732/v1","title":"Real-time traffic sign detection network based on Swin Transformer","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Computer science; Transformer; Computation; Artificial intelligence; Traffic sign; Residual; Traffic sign recognition; Real-time computing; Sign (mathematics); Pattern recognition (psychology); Voltage; Engineering; Algorithm","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":[0.001929999,0.0003888934,0.0003936725,0.0005692602,0.0006797348,0.0003296794,0.002065286,0.000426381,0.00004940155],"category_scores_gemma":[0.0001240292,0.0003867606,0.0002695365,0.002646424,0.000134809,0.0001826496,0.0004841966,0.002265223,0.001506598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004635821,"about_ca_system_score_gemma":0.0004030003,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004943154,"about_ca_topic_score_gemma":0.00007255209,"domain_scores_codex":[0.9942738,0.0007766003,0.0004553004,0.001534151,0.001674382,0.001285778],"domain_scores_gemma":[0.9951687,0.001860329,0.0001185831,0.002156419,0.0003711022,0.000324837],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000050842,0.00009195313,0.000006160202,0.000139511,0.00001610239,0.00002949598,0.00009496786,0.9095092,0.000988054,0.0009585075,0.005527566,0.08258762],"study_design_scores_gemma":[0.0002767138,0.0003796068,0.001044702,0.0004597739,0.000006629397,0.000001930445,0.00001044875,0.9812917,0.001124348,0.01126536,0.003698602,0.0004402203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009879649,0.0001089918,0.9697269,0.006541789,0.0008335849,0.005149223,0.00006622529,0.003354984,0.004338704],"genre_scores_gemma":[0.9685034,0.0006328447,0.02315089,0.0001194112,0.001548859,0.003132648,0.0001918301,0.0002024763,0.002517627],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9586238,"threshold_uncertainty_score":0.9998584,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0705037209025731,"score_gpt":0.3716487848741072,"score_spread":0.3011450639715341,"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."}}