{"id":"W2920160392","doi":"10.1109/tii.2019.2901934","title":"Feature Extraction of Constrained Dynamic Latent Variables","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Latent variable; Feature extraction; Feature (linguistics); Dynamic data; Boundary (topology); Inference; Artificial intelligence; Bayesian probability; Bayesian inference; Data mining; Machine learning; Pattern recognition (psychology); Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.0001390858,0.0001573246,0.000247901,0.0001904124,0.00004642847,0.00003328389,0.00008867147,0.0003230901,0.0001740534],"category_scores_gemma":[0.000003618107,0.0001509799,0.0001078733,0.0002544427,0.00002366493,0.0002692945,3.582203e-7,0.0004814406,0.0001318509],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001063049,"about_ca_system_score_gemma":0.00003799451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001450443,"about_ca_topic_score_gemma":0.000008450914,"domain_scores_codex":[0.9991115,0.00002004303,0.0004326733,0.00005899452,0.0002045049,0.0001722701],"domain_scores_gemma":[0.9995089,0.00007718857,0.0001031046,0.0002031104,0.00004548229,0.00006222667],"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":[0.00009501335,0.00004613664,0.000007776492,0.0001266595,0.0001896989,5.599251e-7,0.0003805835,0.9625779,0.01661902,0.00005307183,0.0007885958,0.01911492],"study_design_scores_gemma":[0.00233022,0.00018611,0.000008898803,0.0001333579,0.00005779732,0.00003337692,0.0008303552,0.960507,0.02727249,0.000008485362,0.00838841,0.0002434637],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5516357,0.00002402301,0.4244747,0.0001487487,0.01160961,0.001274236,0.0002409504,0.0007190822,0.009872938],"genre_scores_gemma":[0.9987759,0.00001963805,0.0002681579,0.00001916816,0.00003986645,0.0000209918,0.000005416814,0.00001762646,0.0008332069],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4471402,"threshold_uncertainty_score":0.6156784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01313634010620046,"score_gpt":0.2211130558235808,"score_spread":0.2079767157173803,"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."}}