{"id":"W4293863184","doi":"10.1109/siu55565.2022.9864871","title":"Tyre (Tire) Brand and Size Detection with Computer Vision","year":2022,"lang":"en","type":"article","venue":"2022 30th Signal Processing and Communications Applications Conference (SIU)","topic":"Vehicle License Plate Recognition","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Stantec (Canada)","funders":"","keywords":"Computer science; Computer vision; Automotive engineering; Engineering","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.000192401,0.0001728222,0.000165702,0.00008984021,0.00130486,0.0002059352,0.0003142834,0.00005282302,0.00007418497],"category_scores_gemma":[0.00000251264,0.0001820386,0.00001906602,0.00047011,0.0001741882,0.0002580275,0.0002394936,0.0004554873,0.000006760852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005631293,"about_ca_system_score_gemma":0.00005090144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002120821,"about_ca_topic_score_gemma":0.00005225113,"domain_scores_codex":[0.9990426,0.00008286534,0.0002371192,0.0002753547,0.000181613,0.0001804857],"domain_scores_gemma":[0.9990626,0.0001655585,0.00008676252,0.000470732,0.0001270273,0.00008730776],"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":[0.00002256619,0.00007925489,0.0001235048,0.00009430332,0.0000259206,5.577912e-7,0.0005875513,0.0007379476,0.008595889,0.0004562926,0.0000438345,0.9892324],"study_design_scores_gemma":[0.000684479,0.0002037374,0.002264757,0.00007947381,0.00008554085,0.0001738073,0.001299116,0.9702056,0.0005987125,0.00120457,0.02274423,0.0004559961],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4994131,0.00648937,0.4808423,0.001664129,0.00005407079,0.001910404,0.0001478948,0.001399693,0.008078996],"genre_scores_gemma":[0.9930929,0.0005343776,0.005146607,0.00007509877,0.00002826089,0.0009371662,0.00008358574,0.00003212938,0.000069878],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9887764,"threshold_uncertainty_score":0.9999953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01131531485695115,"score_gpt":0.2211075374791361,"score_spread":0.2097922226221849,"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."}}