{"id":"W2771729497","doi":"10.1002/aic.16059","title":"Extracting dynamic features with switching models for process data analytics and application in soft sensing","year":2017,"lang":"en","type":"article","venue":"AIChE Journal","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; Alberta Innovates - Technology Futures","keywords":"Process (computing); Computer science; Representation (politics); Latent variable; Context (archaeology); Data mining; Analytics; Soft sensor; Machine learning; Probabilistic logic; Feature (linguistics); Bayesian probability; Artificial intelligence; Construct (python library)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003765861,0.000078965,0.0001187329,0.00004710303,0.0003111253,0.000295388,0.0001633804,0.00004839837,3.149657e-7],"category_scores_gemma":[0.0000466404,0.00006824205,0.00001183761,0.00002453352,0.000008659776,0.0005637259,0.00001619369,0.0002643098,3.216502e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003018472,"about_ca_system_score_gemma":0.00001605507,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004574959,"about_ca_topic_score_gemma":0.001176379,"domain_scores_codex":[0.9995141,0.000007301202,0.0001386665,0.0001138415,0.00009036512,0.0001357424],"domain_scores_gemma":[0.9995201,0.0000342221,0.000115069,0.0002500123,0.00003633508,0.00004429091],"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":[0.00007351067,0.00001102219,0.001504897,0.0001838609,0.0001018666,0.00001289018,0.001285937,0.5229101,0.01098719,0.00001384242,0.00006224471,0.4628527],"study_design_scores_gemma":[0.0004948769,0.000009562269,0.001494142,0.00009566238,0.00002119063,0.0003024942,0.00050966,0.9962982,0.00004458465,0.00054036,0.00009835136,0.00009093956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2558065,0.0003378942,0.7432737,0.0001511504,0.00008342715,0.0001401567,0.00000233026,0.0000331441,0.0001717564],"genre_scores_gemma":[0.9975246,0.00002905002,0.002294262,0.00001151031,0.0001038607,0.00000186766,0.000002302966,0.00001903676,0.00001355388],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7417181,"threshold_uncertainty_score":0.2848434,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02134140281147947,"score_gpt":0.2913420792251734,"score_spread":0.270000676413694,"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."}}