{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002220971,0.0001635507,0.0002023236,0.0001279994,0.0002054839,0.00006677234,0.00003726526,0.0001751361,0.00004688],"category_scores_gemma":[0.0004059009,0.0001719996,0.0001064651,0.000398002,0.00001124487,0.00005626864,0.00001912537,0.0004964531,0.0001212927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001607664,"about_ca_system_score_gemma":0.00001700835,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002994233,"about_ca_topic_score_gemma":0.00001452286,"domain_scores_codex":[0.9989137,0.0001486205,0.0002106829,0.000227223,0.0002008696,0.0002989226],"domain_scores_gemma":[0.9994749,0.0001548163,0.00004652065,0.0001556721,0.00009372364,0.00007430663],"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":[0.000008609458,0.000006886303,0.0002303856,0.00002454845,0.0000362796,0.00009733906,0.0005421616,0.9346085,0.04955148,0.00001533745,0.0000150296,0.01486344],"study_design_scores_gemma":[0.0006046718,0.0001254191,0.001009324,0.0002219695,0.0000347582,0.0001742323,0.002565534,0.656073,0.3352737,0.0000293578,0.003435538,0.0004525102],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9907194,0.0001008544,0.001650656,0.000003502467,0.001172553,0.00009722792,8.885086e-7,0.0005047563,0.005750143],"genre_scores_gemma":[0.999088,0.000006430012,0.0002658501,0.00001798401,0.0003405143,0.00000231381,0.00000142754,0.0000535788,0.0002238896],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2857223,"threshold_uncertainty_score":0.7013942,"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."}}