{"id":"W2058662333","doi":"10.1002/aic.13735","title":"Identification of nonlinear parameter varying systems with missing output data","year":2012,"lang":"en","type":"article","venue":"AIChE Journal","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":81,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Identification (biology); Computation; Nonlinear system; Particle filter; Computer science; Likelihood function; Missing data; Function (biology); Filter (signal processing); Work (physics); Algorithm; System identification; Scale (ratio); Mathematical optimization; Estimation theory; Engineering; Mathematics; Data mining; Machine learning","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":[0.001222554,0.0005546823,0.0009079535,0.000362452,0.000316353,0.0006333538,0.000582562,0.0009883704,0.0006325413],"category_scores_gemma":[0.002797183,0.0003233853,0.000406949,0.0003274235,0.0006135313,0.0005599929,0.0005944003,0.0007387527,0.0001744982],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003301565,"about_ca_system_score_gemma":0.0005505431,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003056857,"about_ca_topic_score_gemma":0.001835118,"domain_scores_codex":[0.9996284,0.0001124811,0.00002395966,0.00009160777,0.0001106615,0.00003280211],"domain_scores_gemma":[0.9990583,0.0005409202,0.0001573105,0.0001030316,0.0001178272,0.00002264516],"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":[0.0002168612,0.00007404765,0.001775822,0.0001328383,0.00006603438,0.0003047627,0.000101355,0.9303909,0.01448116,0.003148551,0.0003759791,0.04893174],"study_design_scores_gemma":[0.000007839,0.00002176437,0.0003841599,0.000002782892,0.000003569438,0.00001412164,0.000003716106,0.9965062,0.002142073,0.0007697887,0.0001396123,0.000004428533],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08022233,0.0001200504,0.9181026,0.0001130128,0.00004220772,0.00004444101,0.00005048425,0.0003342124,0.0009707314],"genre_scores_gemma":[0.9468051,0.00006429072,0.05202081,0.00002562227,0.00001108302,0.00004845075,0.00007382745,0.00001324679,0.0009375659],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003056857,"threshold_uncertainty_score":0.006465554,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03161202388398695,"score_gpt":0.2575776022582443,"score_spread":0.2259655783742574,"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."}}