{"id":"W3201388859","doi":"10.3390/pr9101691","title":"Polymethyl Methacrylate Quality Modeling with Missing Data Using Subspace Based Model Identification","year":2021,"lang":"en","type":"article","venue":"Processes","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"McMaster University","keywords":"Partial least squares regression; Subspace topology; Missing data; Principal component analysis; Computer science; Identification (biology); Process (computing); Least-squares function approximation; Polymethyl methacrylate; Component (thermodynamics); Algorithm; Quality (philosophy); Data mining; Artificial intelligence; Mathematics; Machine learning; Statistics; Materials science","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":[0.000368108,0.0001393253,0.0001878838,0.00005954901,0.0001143912,0.0001771501,0.0001851706,0.00005890013,0.000006783836],"category_scores_gemma":[0.000182532,0.000134422,0.00001998895,0.0004385853,0.00001228727,0.0004502115,0.00002592695,0.0001072158,0.00000361522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004387697,"about_ca_system_score_gemma":0.0002125688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007979984,"about_ca_topic_score_gemma":0.0002386681,"domain_scores_codex":[0.9989181,0.0000468115,0.0003049432,0.0003144118,0.0002296904,0.0001860209],"domain_scores_gemma":[0.9990976,0.0000490439,0.00006472088,0.0005197173,0.0002063207,0.00006261448],"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":[0.00001195287,0.00001291396,0.00002808764,0.0008837162,0.00002902825,0.000002117047,0.0001228877,0.9227902,0.07465619,0.000008111474,0.000004321662,0.001450468],"study_design_scores_gemma":[0.0002671457,0.000002165754,0.000002696559,0.00009045433,0.00003880924,0.000007501124,0.0002346377,0.9519144,0.04710387,0.00007539184,0.00008243492,0.0001804814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.104393,0.004543873,0.8902768,0.0001044145,0.00008940411,0.00007690449,0.00002316545,0.0003018594,0.0001905617],"genre_scores_gemma":[0.9896125,0.00003797245,0.01012493,0.00003593357,0.00004731687,0.00000976854,0.00003334409,0.00003788742,0.00006034911],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8852195,"threshold_uncertainty_score":0.5481569,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1053061967832261,"score_gpt":0.327053911748887,"score_spread":0.221747714965661,"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."}}