{"id":"W2065458594","doi":"10.1002/cjce.20099","title":"Treatment of missing values in process data analysis","year":2008,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":85,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Missing data; Outlier; Univariate; Data mining; Computer science; Multivariate statistics; Process (computing); Data analysis; Principal component analysis; Statistics; Artificial intelligence; Mathematics; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0002135843,0.00006903076,0.0002905861,0.0001359434,0.00002029902,0.00000789722,0.0002639299,0.00003056168,0.00002550354],"category_scores_gemma":[0.001478426,0.00004512578,0.0000511894,0.0003083644,0.00005337006,0.00003865116,0.000006806342,0.00009592995,1.556665e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008908098,"about_ca_system_score_gemma":0.0002373322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006743863,"about_ca_topic_score_gemma":0.0001139258,"domain_scores_codex":[0.9993603,0.00001781687,0.0003108007,0.00006100917,0.0001127417,0.0001373347],"domain_scores_gemma":[0.9989705,0.0005120619,0.00009265443,0.0002051615,0.00005910311,0.0001605155],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005348732,0.002071972,0.1320586,0.003181983,0.01588886,0.009060392,0.1012142,0.1107286,0.1962574,0.1541988,0.002007949,0.2727964],"study_design_scores_gemma":[0.002086618,0.0003537374,0.008789733,0.0006439189,0.002160628,0.00121669,0.000218212,0.6234586,0.1992721,0.1607493,0.0002772612,0.0007732357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9650029,0.0003029265,0.03425336,0.0002205338,0.00003745682,0.00004137001,0.00002057234,0.00000318586,0.0001176947],"genre_scores_gemma":[0.9364634,0.000003555741,0.06347741,0.000004260459,0.00004056326,4.418568e-7,0.000001015155,0.000006089511,0.000003225966],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5127299,"threshold_uncertainty_score":0.1840176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1247660004950022,"score_gpt":0.3509200373500492,"score_spread":0.2261540368550471,"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."}}