{"id":"W4361829816","doi":"10.1109/iccsse55346.2022.10079777","title":"Defect Detection for Printed Circuit Board Assembly Using Deep Learning","year":2022,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Deep learning; Printed circuit board; Artificial intelligence; Computer science; Task (project management); Pattern recognition (psychology); Sampling (signal processing); Layer (electronics); Computer vision; Machine learning; Engineering; Materials science","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":[],"consensus_categories":[],"category_scores_codex":[0.0004644314,0.0001222063,0.0001590275,0.0001777038,0.000464988,0.00005195677,0.00006232336,0.00006856128,0.0001297316],"category_scores_gemma":[0.00006316035,0.0001328589,0.0001700103,0.0003141654,0.000004232225,0.00009242419,0.00003232461,0.0003194074,0.00001227983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003227325,"about_ca_system_score_gemma":0.00001050502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009778238,"about_ca_topic_score_gemma":0.00002669965,"domain_scores_codex":[0.999076,0.00007892244,0.0002370454,0.000184136,0.0001863511,0.0002375263],"domain_scores_gemma":[0.9996682,0.00007958803,0.00004766274,0.0001161371,0.00004255706,0.00004585125],"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":[0.00004421821,0.00001184033,0.0001751427,0.00003651459,0.00007550256,0.000002374245,0.0001503597,0.5445784,0.376582,0.0001060088,0.0001793558,0.07805834],"study_design_scores_gemma":[0.0007815562,0.0003018659,0.0001626864,0.00000964218,0.00003426653,0.00006154001,0.0006853995,0.845085,0.04669183,0.00005623403,0.1058237,0.000306226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4833307,0.00004633232,0.5126027,0.000002159738,0.001308758,0.0003427157,0.000001350078,0.0005532196,0.001812056],"genre_scores_gemma":[0.9992196,0.000001230494,0.0001669705,0.00002227196,0.0001653348,0.0001287164,0.000003302436,0.00004877778,0.000243777],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5158889,"threshold_uncertainty_score":0.541783,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03385775727346523,"score_gpt":0.2447080288739208,"score_spread":0.2108502716004556,"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."}}