{"id":"W2012519640","doi":"10.1109/med.2007.4433899","title":"Statistical process control using kernel PCA","year":2007,"lang":"en","type":"article","venue":"","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Control chart; Kernel principal component analysis; Kernel (algebra); Computer science; Control limits; Statistical process control; Outlier; Artificial intelligence; Principal component analysis; Pattern recognition (psychology); Chart; Kernel method; Process control; Multivariate statistics; Variable kernel density estimation; Radar chart; Process (computing); Statistics; Mathematics; Machine learning; Support vector machine","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002517447,0.0001639627,0.0003093198,0.0001578661,0.0001994879,0.0001704346,0.0005056223,0.0000729354,0.0007974651],"category_scores_gemma":[0.01113947,0.0001177118,0.00003840898,0.0006050433,0.0002017113,0.0004166948,0.00005697502,0.0002050862,0.0003477884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006991259,"about_ca_system_score_gemma":0.00008459033,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001995628,"about_ca_topic_score_gemma":0.00001655799,"domain_scores_codex":[0.9962862,0.00005553507,0.0008031267,0.0005547919,0.001735343,0.0005649982],"domain_scores_gemma":[0.9934785,0.005135377,0.0001593729,0.0003227862,0.0005754709,0.0003285362],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0008608452,0.0003860834,0.1591419,0.00005949975,0.00006052417,0.0006152246,0.0008194103,0.01134843,0.005195271,0.3205982,0.001796563,0.4991181],"study_design_scores_gemma":[0.001662594,0.0001281481,0.04060645,0.00002418228,0.00003327981,0.00005059026,0.002275235,0.10686,0.003642221,0.8413101,0.002769805,0.0006374338],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03055595,0.00003274078,0.9557943,0.00006427557,0.0003614313,0.000143682,0.00002593847,0.00008329986,0.01293836],"genre_scores_gemma":[0.9029992,3.403731e-7,0.09599379,0.0002013376,0.0001933092,0.000002500898,8.077855e-7,0.00001596075,0.0005928089],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8724432,"threshold_uncertainty_score":0.9971901,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1490797877599652,"score_gpt":0.5024151587663134,"score_spread":0.3533353710063482,"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."}}