{"id":"W2102384358","doi":"10.1002/aic.14866","title":"Nonlinear process identification in the presence of multiple correlated hidden scheduling variables with missing data","year":2015,"lang":"en","type":"article","venue":"AIChE Journal","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates - Technology Futures","keywords":"Missing data; Computer science; Weighting; Smoothing; Nonlinear system; Scheduling (production processes); Computation; Generality; Algorithm; Maximization; Mathematical optimization; Machine learning; Mathematics","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":[0.001059767,0.0000686085,0.0001068921,0.00006257353,0.00004676091,0.00009030963,0.0003723078,0.00004581641,0.000003061783],"category_scores_gemma":[0.0003199656,0.00004539046,0.0000108626,0.0002583332,0.00001716868,0.0003558395,0.00001313848,0.0002819071,0.000004075394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000251577,"about_ca_system_score_gemma":0.0000639268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004352047,"about_ca_topic_score_gemma":0.00004558665,"domain_scores_codex":[0.9991856,0.00007232371,0.0002927695,0.00008417264,0.0002519632,0.0001131775],"domain_scores_gemma":[0.9994027,0.00007997033,0.0001088285,0.0002495904,0.0001113775,0.00004754849],"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":[0.0002590999,0.0001797198,0.02551929,0.0002391252,0.000209176,0.00005020463,0.01186721,0.8989665,0.04434687,0.000017585,0.001059496,0.01728574],"study_design_scores_gemma":[0.0007048045,0.00002108874,0.0007157206,0.0001442104,0.0000178924,0.0001850409,0.002576978,0.9938793,0.001262678,0.00004649499,0.0003813568,0.00006442476],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9393777,0.0009003298,0.05841096,0.0002007347,0.0004522509,0.000203598,0.000007217979,0.00005545433,0.0003917639],"genre_scores_gemma":[0.9983624,0.00001153504,0.001464318,0.000009430552,0.0001138838,0.000003241654,0.00000653506,0.00001121498,0.00001740293],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09491283,"threshold_uncertainty_score":0.1850969,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0331529056048574,"score_gpt":0.2696288637583033,"score_spread":0.2364759581534459,"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."}}