{"id":"W4312689237","doi":"10.1115/detc2022-89921","title":"Normalization and Dimension Reduction for Machine Learning in Advanced Manufacturing","year":2022,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dimensionality reduction; Normalization (sociology); Computer science; Leverage (statistics); Pharmaceutical manufacturing; Principal component analysis; Cellular manufacturing; Analytics; Database normalization; Data analysis; Artificial intelligence; Data mining; Machine learning; Manufacturing engineering; Pattern recognition (psychology); Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001826361,0.00005272555,0.0000694027,0.0001141577,0.0001372752,0.00001110365,0.00001337425,0.00002535209,0.00003299834],"category_scores_gemma":[0.000008401188,0.00005482567,0.00001394432,0.00007996074,0.00000189632,0.0000953424,0.00001658856,0.0001148393,6.917172e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007009298,"about_ca_system_score_gemma":0.0000020758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004634455,"about_ca_topic_score_gemma":0.000009916341,"domain_scores_codex":[0.999614,0.00002307073,0.0001259389,0.00008911426,0.00006403372,0.00008383982],"domain_scores_gemma":[0.9999119,0.00001241844,0.00001857147,0.00003645852,0.000006188075,0.00001446522],"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":[0.00005420106,0.000004810063,0.0002653482,0.00002725146,0.000003541409,5.401884e-7,0.0001917115,0.9074655,0.04051258,0.00005810113,0.00006990237,0.0513465],"study_design_scores_gemma":[0.002034786,0.0002994877,0.0014753,0.00002316512,0.000007190397,0.00006039833,0.001087893,0.80597,0.1419797,0.0001139504,0.04667223,0.0002758716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9893368,0.0001054596,0.009153072,0.00001242328,0.0005468445,0.0002617648,0.000001607506,0.0001731992,0.0004087846],"genre_scores_gemma":[0.9993611,0.00001184407,0.0001913602,0.000003754706,0.00003910059,0.00006002047,0.00001516375,0.00001328603,0.0003043437],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1014955,"threshold_uncertainty_score":0.2235726,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009365726087279784,"score_gpt":0.2086932423415717,"score_spread":0.199327516254292,"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."}}