{"id":"W1985862691","doi":"10.1002/cjce.20557","title":"Parameter estimation in models with hidden variables : An application to a biotech process","year":2011,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Control Systems and Identification","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of British Columbia","funders":"","keywords":"Gibbs sampling; Sampling (signal processing); Bayesian probability; Nonlinear system; Process (computing); Stochastic process; Mathematics; Computer science; Estimation theory; Metropolis–Hastings algorithm; Algorithm; Set (abstract data type); Mathematical optimization; Artificial intelligence; Statistics; Markov chain Monte Carlo","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000185756,0.00007796447,0.0001119192,0.0001531942,0.00001527492,0.00003676281,0.0001756116,0.00005150919,0.000003775669],"category_scores_gemma":[0.00002802721,0.00005989778,0.00001569559,0.0002090657,0.000008014542,0.0002280572,0.000002030922,0.000147613,0.000001564997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001378024,"about_ca_system_score_gemma":0.00006241407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001694384,"about_ca_topic_score_gemma":0.001018857,"domain_scores_codex":[0.9994845,0.000004810605,0.0002151755,0.00006124935,0.00008619503,0.0001480046],"domain_scores_gemma":[0.9995812,0.00001415151,0.00003488954,0.0001248694,0.00005934878,0.0001855893],"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":[0.000009729616,0.000005039964,0.00005088276,0.00003555054,0.00001795878,0.000003378829,0.001738758,0.9467313,0.04676339,0.0008182281,0.00001022464,0.003815537],"study_design_scores_gemma":[0.000139777,0.00001688446,0.0002437637,0.00007933332,0.0000119046,0.00003397068,0.00002070524,0.9724665,0.02570298,0.001165595,0.00001997154,0.0000985877],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7527968,0.00005995846,0.2467907,0.00005391185,0.00004790388,0.0001519777,0.000001588676,0.00002189542,0.00007533851],"genre_scores_gemma":[0.9950717,3.042531e-7,0.004839586,0.000008519943,0.00003812261,0.00002118159,0.000001350742,0.00001796083,0.000001290593],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2422749,"threshold_uncertainty_score":0.2561413,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01046181038874812,"score_gpt":0.1812168439554037,"score_spread":0.1707550335666556,"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."}}