{"id":"W2955682084","doi":"","title":"Optimizing process parameters to increase the quality of the output in a separator : An application of Deep Kernel Learning in combination with the Basin-hopping optimizer","year":2019,"lang":"en","type":"article","venue":"KTH Publication Database DiVA (KTH Royal Institute of Technology)","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Process (computing); Artificial intelligence; Deep learning; Quality (philosophy); Machine learning; Process engineering; Environmental science; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00108135,0.0001630963,0.0003090395,0.0004104174,0.0000771361,0.00002873165,0.0007311542,0.0001263437,0.000003382682],"category_scores_gemma":[0.0004223909,0.0001045946,0.00004344916,0.001611232,0.0001752365,0.0004066872,0.0001085557,0.0003821408,0.000002985381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009177822,"about_ca_system_score_gemma":0.00006242051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009771571,"about_ca_topic_score_gemma":0.0007627424,"domain_scores_codex":[0.9984767,0.0001249845,0.0006265889,0.0002800538,0.0002968918,0.0001947629],"domain_scores_gemma":[0.9983395,0.00007078094,0.0004027751,0.0008921914,0.0002581938,0.00003657402],"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":[0.0001357298,0.0001762247,0.03290851,0.0002303213,0.00004867129,2.200636e-7,0.0007795088,0.9457708,0.004941423,0.004289367,0.0000398978,0.01067933],"study_design_scores_gemma":[0.001835116,0.0001012948,0.01856203,0.0002066378,0.00002595901,0.000002727121,0.002562697,0.9659907,0.007438452,0.00002817342,0.002997332,0.0002488635],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9665941,0.00005270959,0.0294479,0.001685191,0.00008334945,0.001474147,0.00002680555,0.0001377219,0.0004980846],"genre_scores_gemma":[0.9977104,0.000003379102,0.00168429,0.00006709428,0.000006060169,0.0004329909,0.00005353615,0.00001704842,0.00002520075],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03111631,"threshold_uncertainty_score":0.4265245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01180795159629289,"score_gpt":0.2570071105052332,"score_spread":0.2451991589089403,"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."}}