{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006623079,0.00008636365,0.000155454,0.00007715444,0.00006358637,0.00009557156,0.0001859792,0.00004929971,0.000007066691],"category_scores_gemma":[0.0000455671,0.00006721576,0.00002659086,0.00009995748,0.00001214554,0.0005029025,0.00001483603,0.0001916416,0.00002138572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003542623,"about_ca_system_score_gemma":0.000013151,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001121805,"about_ca_topic_score_gemma":9.107898e-7,"domain_scores_codex":[0.9991122,0.00004790939,0.0003808841,0.00006939547,0.0002056,0.0001840132],"domain_scores_gemma":[0.9993511,0.0000459663,0.0001426704,0.0003096634,0.00005212623,0.00009849552],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002525941,0.0003702911,0.03269965,0.001535806,0.002316193,0.00003918753,0.005059717,0.1931701,0.6166577,0.00009394278,0.01178438,0.1360205],"study_design_scores_gemma":[0.0005680383,0.00002811155,0.001280183,0.0001683155,0.00008781994,0.0007357994,0.0003534909,0.9778045,0.004532836,0.000004160638,0.01425976,0.0001769954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6903068,0.007374306,0.295617,0.0001127489,0.004564256,0.0002959822,0.00003096477,0.0002096429,0.001488262],"genre_scores_gemma":[0.9985926,0.00002368821,0.0006553341,0.000008841058,0.0005753735,0.000002139945,0.000004460067,0.00002163317,0.0001158875],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7846344,"threshold_uncertainty_score":0.2740979,"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."}}