{"id":"W2137722086","doi":"10.1002/aic.14663","title":"A <scp>B</scp>ayesian framework for real‐time identification of locally weighted partial least squares","year":2014,"lang":"en","type":"article","venue":"AIChE Journal","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Alberta Innovates - Technology Futures","keywords":"Overfitting; Partial least squares regression; Similarity (geometry); Model selection; Computer science; Bayesian probability; Function (biology); Identification (biology); Artificial intelligence; Selection (genetic algorithm); Mathematics; Least-squares function approximation; Bayes' theorem; Algorithm; Data mining; Machine learning; Statistics; Image (mathematics); Artificial neural network","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":[0.002505643,0.001451825,0.0008103005,0.001240503,0.0005560557,0.001664645,0.001468896,0.00147417,0.006821292],"category_scores_gemma":[0.003169538,0.0005144233,0.001194103,0.001330875,0.001789705,0.001598999,0.001437751,0.002348784,0.00284878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001003517,"about_ca_system_score_gemma":0.001386341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007515958,"about_ca_topic_score_gemma":0.005996199,"domain_scores_codex":[0.9989059,0.0004570993,0.0000538257,0.0002464213,0.0002763093,0.00006050003],"domain_scores_gemma":[0.9989063,0.0004775336,0.0001700534,0.0001208319,0.0002814078,0.00004390379],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00008338245,0.00008941656,0.0007837379,0.0003593261,0.0001621173,0.0003689244,0.000217529,0.371801,0.005719081,0.459104,0.008678804,0.1526328],"study_design_scores_gemma":[0.000008242469,0.00005088366,0.0003727286,0.00003101918,0.00002273389,0.0000997835,0.00001558612,0.9092994,0.0006551346,0.07911322,0.01030181,0.00002945634],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005481456,0.0003554132,0.9970887,0.0001681462,0.00005246071,0.00001471011,0.00003411366,0.00005571944,0.001682677],"genre_scores_gemma":[0.1906265,0.003391717,0.7749243,0.0004386473,0.0007039509,0.0006071959,0.0004336852,0.0003604172,0.02851365],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007515958,"threshold_uncertainty_score":0.02281952,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005840339118026752,"score_gpt":0.2249979968814746,"score_spread":0.2191576577634478,"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."}}