{"id":"W2284118625","doi":"10.1021/acs.iecr.5b03397","title":"An Alternative Formulation of PCA for Process Monitoring Using Distance Correlation","year":2015,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Research and Development Corporation of Newfoundland and Labrador; Natural Sciences and Engineering Research Council of Canada; Atlantic Canada Opportunities Agency; Canada Research Chairs; University of Tasmania","keywords":"Principal component analysis; Kernel principal component analysis; Gaussian process; Mathematics; Multivariate normal distribution; Pattern recognition (psychology); Gaussian; Transformation (genetics); Gaussian function; Kernel (algebra); Invariant (physics); Multivariate statistics; Correlation; Correlation coefficient; Artificial intelligence; Computer science; Kernel method; Statistics; Support vector machine","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":[0.0006358604,0.0001238021,0.0001755528,0.00008520095,0.00004794524,0.00004765264,0.0001664136,0.0001917436,0.000003476598],"category_scores_gemma":[0.0003429748,0.0001398798,0.00003764713,0.0003398156,0.00001594076,0.0002540464,0.00001021091,0.0003484214,8.860577e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003365505,"about_ca_system_score_gemma":0.00007218473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003687427,"about_ca_topic_score_gemma":5.028764e-7,"domain_scores_codex":[0.998808,0.00001745969,0.0002884546,0.0001645312,0.0004301514,0.0002913821],"domain_scores_gemma":[0.9991852,0.0000962245,0.00004420158,0.0001758959,0.0003490706,0.0001494304],"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":[0.00005162824,0.000009506959,0.0005218707,0.00008757835,0.00001865636,5.537908e-7,0.0001864284,0.6458027,0.352353,0.00001075319,0.00001258322,0.0009447274],"study_design_scores_gemma":[0.0006232988,0.00002743768,0.00001092854,0.00008412539,0.000003868478,0.000001877286,0.0001941513,0.5854986,0.4132391,0.00002060028,0.000215394,0.00008062128],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9766599,0.00008589443,0.02181426,0.000004216633,0.0006758183,0.0003774882,0.00001927234,0.000157804,0.00020535],"genre_scores_gemma":[0.9985505,0.000001398897,0.0000730799,1.148295e-7,0.001210218,0.00007522717,0.00001377656,0.00003800106,0.00003770544],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06088602,"threshold_uncertainty_score":0.5704134,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1513856825870648,"score_gpt":0.384333727471686,"score_spread":0.2329480448846211,"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."}}