{"id":"W2066409131","doi":"10.1021/ie990821g","title":"A New Criterion for Modeling Distillation Column Data Using Commercial Simulators","year":2000,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Process Optimization and Integration","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Column (typography); Distillation; Fractionating column; Computer science; Fortran; Matching (statistics); Field (mathematics); Calculator; Function (biology); Troubleshooting; Software; Algorithm; Mathematics; Statistics; Programming language","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0004308231,0.000170024,0.0001787372,0.00007871724,0.0001350254,0.0001898811,0.0003943934,0.0002851083,0.0003742242],"category_scores_gemma":[0.0003785311,0.0002030019,0.00003772516,0.0004249622,0.00001558429,0.0003851355,0.00005123635,0.0004971271,0.000006573974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000222169,"about_ca_system_score_gemma":0.0001406131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007885914,"about_ca_topic_score_gemma":0.000002466305,"domain_scores_codex":[0.9986363,0.00001306159,0.0003206204,0.0002877428,0.0003352902,0.0004069838],"domain_scores_gemma":[0.999181,0.00009824569,0.00001500744,0.0003985686,0.0001362639,0.0001708802],"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":[0.00003844675,0.000007422811,0.00001157126,0.00006032603,0.00001676356,5.517009e-7,0.00004254068,0.9438311,0.04065665,0.000005545654,0.001823582,0.01350554],"study_design_scores_gemma":[0.000640901,0.00001119969,0.000001644837,0.00009270982,0.00001056882,0.000002559685,0.00001572269,0.9691623,0.01921254,0.00001444828,0.01064172,0.0001936749],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6615751,0.0003142894,0.3338507,0.0001495604,0.0005923687,0.0009723695,0.0002728776,0.0008409026,0.001431898],"genre_scores_gemma":[0.9956349,0.00003501031,0.001787336,0.000003788029,0.001615867,0.00001820312,0.0005259605,0.00007015024,0.0003087581],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3340598,"threshold_uncertainty_score":0.8278176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3014194510493796,"score_gpt":0.3967251989937178,"score_spread":0.09530574794433821,"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."}}