{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002097161,0.0008647701,0.0005441523,0.001049287,0.0001655302,0.0007017797,0.0009525937,0.00103632,0.002672282],"category_scores_gemma":[0.0005096054,0.0002956548,0.000796492,0.0006778067,0.0002735774,0.0008949204,0.000543623,0.0008851566,0.002899152],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005083859,"about_ca_system_score_gemma":0.0003901056,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004443932,"about_ca_topic_score_gemma":0.007748476,"domain_scores_codex":[0.9997727,0.00001306735,0.000007325085,0.00009075891,0.00007000589,0.00004625783],"domain_scores_gemma":[0.999837,0.00002423901,0.00001913552,0.0000371497,0.00006953662,0.00001290815],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004228046,0.0005443102,0.005970178,0.0002656391,0.0001316512,0.0002386036,0.00005813454,0.03440409,0.1116884,0.001482683,0.01740336,0.8273902],"study_design_scores_gemma":[0.00001624339,0.0002155061,0.009704954,0.00003245737,0.0000554913,0.0004153529,0.00006463685,0.883669,0.09645411,0.002106177,0.007225831,0.00004032749],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1717593,0.001079031,0.7992079,0.0004280275,0.0003191518,0.0002528004,0.00209177,0.009324699,0.01553724],"genre_scores_gemma":[0.6520689,0.0009953644,0.3166182,0.0004559257,0.0001182655,0.0001689932,0.005356987,0.0002599821,0.02395748],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004443932,"threshold_uncertainty_score":0.008939683,"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."}}