{"id":"W3159307704","doi":"10.1016/j.chemolab.2021.104315","title":"Mixture robust semi-supervised probabilistic principal component regression with missing input data","year":2021,"lang":"en","type":"article","venue":"Chemometrics and Intelligent Laboratory Systems","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Unavailability; Outlier; Principal component analysis; Probabilistic logic; Computer science; Missing data; Principal component regression; Data mining; Expectation–maximization algorithm; Component (thermodynamics); Regression; Maximization; Statistical model; Regression analysis; Robust regression; Artificial intelligence; Machine learning; Pattern recognition (psychology); Statistics; Mathematics; Mathematical optimization; Maximum likelihood","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004841188,0.001958169,0.002853245,0.00109071,0.0007597618,0.001652497,0.003957347,0.002257372,0.001575571],"category_scores_gemma":[0.01219551,0.001788399,0.002918169,0.001598561,0.001498695,0.002517216,0.002794195,0.002878285,0.001643437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000677333,"about_ca_system_score_gemma":0.002085104,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004959849,"about_ca_topic_score_gemma":0.005281767,"domain_scores_codex":[0.9960176,0.001801195,0.0002315739,0.0009340773,0.0007621004,0.0002533731],"domain_scores_gemma":[0.9932768,0.003579678,0.0006478117,0.001253706,0.001104565,0.000137496],"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":[0.00103168,0.0002671876,0.001503608,0.0005409556,0.0006158161,0.0001391372,0.0001952285,0.7354848,0.008688674,0.009910028,0.00408513,0.2375377],"study_design_scores_gemma":[0.00001157917,0.00002563106,0.0002421788,0.000005751052,0.00001801312,0.00001875051,0.000003889691,0.9962753,0.0009842678,0.002110864,0.0002926741,0.0000110803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00627227,0.0002031059,0.9924093,0.00006606182,0.00002750703,0.00002942623,0.0001034053,0.0006834496,0.0002054491],"genre_scores_gemma":[0.4551923,0.0005133137,0.5347797,0.0001738204,0.0002077615,0.0004172592,0.002546978,0.0006054844,0.005563443],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004959849,"threshold_uncertainty_score":0.025603,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03255291854287938,"score_gpt":0.2368165462045662,"score_spread":0.2042636276616868,"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."}}