{"id":"W4393100908","doi":"10.54254/2755-2721/51/20241591","title":"A new frontier in electronics manufacturing: Optimized deep learning techniques for PCB image reconstruction","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Printed circuit board; Electronics; Computer science; Deep learning; Artificial intelligence; Benchmark (surveying); Autofocus; Computer engineering; Engineering; Electrical engineering; Focus (optics)","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":[0.0005765975,0.0006380684,0.0004616311,0.0003138219,0.0001374551,0.0006451606,0.001026038,0.0007448884,0.001091612],"category_scores_gemma":[0.001666888,0.0003241001,0.0003961943,0.0004629651,0.0006123226,0.001210169,0.0006855812,0.001414078,0.0005176875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005896962,"about_ca_system_score_gemma":0.0006438957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002982428,"about_ca_topic_score_gemma":0.003843217,"domain_scores_codex":[0.9997812,0.00005223355,0.000008419245,0.00005516467,0.00007740717,0.00002566724],"domain_scores_gemma":[0.9996935,0.0001184113,0.00003483678,0.00006619187,0.00007193088,0.00001514103],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007549076,0.00006441063,0.0008454004,0.0001528329,0.0000591803,0.00005851694,0.00005317348,0.740979,0.01789452,0.01390277,0.004031955,0.2218827],"study_design_scores_gemma":[0.00000205712,0.00001336735,0.00008842299,0.000006386188,0.000002729936,0.00001481059,0.000004664871,0.9920017,0.003360018,0.003564179,0.0009386324,0.000003039156],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01052832,0.0005348725,0.9869356,0.0002576607,0.00002270622,0.00001295563,0.00006182743,0.0006557922,0.0009902195],"genre_scores_gemma":[0.507288,0.001296818,0.4847365,0.0003849937,0.00006472712,0.00006419646,0.0005857662,0.0003581344,0.005220908],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002982428,"threshold_uncertainty_score":0.005930185,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004411998900722455,"score_gpt":0.1956026766827967,"score_spread":0.1911906777820742,"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."}}