{"id":"W4413248274","doi":"10.1504/ijqet.2024.147893","title":"Harnessing deep learning for quality engineering and technology: innovations in process optimisation, defect detection, and predictive quality control","year":2024,"lang":"en","type":"article","venue":"International Journal of Quality Engineering and Technology","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Quality (philosophy); Process (computing); Model predictive control; Engineering; Control (management); Process control; Manufacturing engineering; Risk analysis (engineering); Computer science; Artificial intelligence; Business","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":[0.001493526,0.0001965718,0.0003948701,0.00167108,0.00006998604,0.0001307295,0.000112126,0.0003627969,0.000001324384],"category_scores_gemma":[0.00194609,0.000200203,0.00005262105,0.0007534939,0.00006110113,0.0003275788,0.00002987064,0.0007338491,2.627049e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001563983,"about_ca_system_score_gemma":0.00002851771,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001481249,"about_ca_topic_score_gemma":0.00001108276,"domain_scores_codex":[0.9984086,0.00004163859,0.0009090003,0.0002295414,0.000211241,0.0001999912],"domain_scores_gemma":[0.9987033,0.0004694019,0.0001720571,0.0000826417,0.000522567,0.00005003401],"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":[0.0002837696,0.0000778693,0.02053576,0.001823494,0.001500072,0.00004180794,0.001790467,0.5290006,0.201078,0.02142784,0.000013665,0.2224266],"study_design_scores_gemma":[0.003732842,0.0004046109,0.01629704,0.001295818,0.0001001288,0.0007608421,0.001936755,0.9480813,0.01897902,0.00460573,0.003067507,0.0007383827],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6999597,0.001859502,0.2964367,0.0004980497,0.0007444398,0.0001728008,0.00001223052,0.0003092331,0.000007386822],"genre_scores_gemma":[0.9987672,0.0001059034,0.0008498448,0.000006273755,0.0001834548,0.0000493557,0.000003222156,0.00002978147,0.000004945054],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4190807,"threshold_uncertainty_score":0.816404,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01624148549262381,"score_gpt":0.3021479659991376,"score_spread":0.2859064805065138,"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."}}