{"id":"W4200127458","doi":"10.3390/s21248480","title":"Detecting Teeth Defects on Automotive Gears Using Deep Learning","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Automotive industry; Process (computing); Scalability; Engineering; Automotive engineering; Visual inspection; Component (thermodynamics); Computer science; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0002751041,0.0007753493,0.0005396602,0.001332879,0.0001787227,0.0004445766,0.0008212419,0.0007361525,0.00101687],"category_scores_gemma":[0.0009100946,0.0003051918,0.0005395156,0.0004097219,0.0002575456,0.0004823605,0.0005539836,0.0004507983,0.0004306096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004445753,"about_ca_system_score_gemma":0.0003107897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004752217,"about_ca_topic_score_gemma":0.007353584,"domain_scores_codex":[0.9997848,0.000015161,0.0000115928,0.00006563491,0.00007298136,0.00004999802],"domain_scores_gemma":[0.9996647,0.0000870463,0.00005802279,0.00004730767,0.0001201514,0.00002278402],"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":[0.0004760708,0.0004113893,0.02806152,0.0002175261,0.0001466687,0.001179178,0.0001527579,0.2118991,0.1200033,0.0007082869,0.006097072,0.6306473],"study_design_scores_gemma":[0.000005868254,0.00006529367,0.005484872,0.00001359757,0.0000174004,0.0001428557,0.00003769837,0.9782637,0.01504383,0.0004246863,0.0004920503,0.000008073569],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8100706,0.0009269112,0.1804941,0.0002544954,0.000102236,0.00006340858,0.0004479502,0.00470829,0.00293188],"genre_scores_gemma":[0.9618539,0.0001633834,0.03566569,0.00007938953,0.00001449932,0.00001436537,0.000492731,0.00005241351,0.001663576],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004752217,"threshold_uncertainty_score":0.009449124,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02165997450917608,"score_gpt":0.2400053371043163,"score_spread":0.2183453625951403,"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."}}