{"id":"W2956119553","doi":"10.25046/aj040323","title":"An Efficient Automotive Paint Defect Detection System","year":2019,"lang":"en","type":"article","venue":"Advances in Science Technology and Engineering Systems Journal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Automotive industry; Computer science; Automotive engineering; Embedded system; Engineering; Aerospace engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.001636867,0.0001798971,0.0002820141,0.002101394,0.000185981,0.0001271699,0.0002381199,0.0002120466,0.000001422666],"category_scores_gemma":[0.00008081203,0.000158799,0.00003773104,0.002056587,0.00009845534,0.0006494587,0.0000242217,0.0005766345,0.00001517786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004357955,"about_ca_system_score_gemma":0.00002542137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004738333,"about_ca_topic_score_gemma":0.000001787926,"domain_scores_codex":[0.9985864,0.00003157229,0.000399708,0.0002753529,0.0002741405,0.00043283],"domain_scores_gemma":[0.9994305,0.00004127802,0.00008124616,0.0002325621,0.0001074766,0.0001069445],"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":[0.000004607868,0.000005412869,0.0010761,0.00009585095,0.000005613281,0.00001264769,0.00009163882,0.9527897,0.03942451,0.001190418,4.147853e-7,0.005303107],"study_design_scores_gemma":[0.0004771259,0.0002723322,0.0008818622,0.0005596729,0.000006545936,0.001912981,0.002107165,0.9822492,0.009989649,0.00002480596,0.001225886,0.0002927561],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9101778,0.002948956,0.0808174,0.000003036598,0.004833694,0.0002567928,9.156428e-7,0.0006427537,0.0003186622],"genre_scores_gemma":[0.999549,0.00005689606,0.0002314497,0.000001036821,0.0001164704,0.00002110807,1.042611e-7,0.00001883128,0.000005125874],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08937119,"threshold_uncertainty_score":0.6475633,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003341229410052984,"score_gpt":0.2168283349812544,"score_spread":0.2134871055712014,"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."}}