{"id":"W3017980959","doi":"10.1002/cjce.23766","title":"Multi‐stage intelligent operation optimization for a hydrocracking fractionation system with a multi‐fractionator series‐parallel structure","year":2020,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Process Optimization and Integration","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Convergence (economics); Computer science; Mathematical optimization; Series (stratigraphy); Distillation; Fractionating column; Algorithm; Process (computing); Mathematics; Chemistry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000835758,0.0001506623,0.0001569584,0.0001050145,0.0001002906,0.0001250574,0.0001420787,0.0000959614,0.00004047557],"category_scores_gemma":[0.0001686368,0.0001184956,0.000049282,0.0001910253,0.00001622324,0.0005500435,0.000003474502,0.0002877203,0.000001189737],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004089248,"about_ca_system_score_gemma":0.0001723094,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005004564,"about_ca_topic_score_gemma":0.0001361998,"domain_scores_codex":[0.9992643,0.000008233727,0.0003260356,0.00009425475,0.0001469397,0.0001602406],"domain_scores_gemma":[0.9992566,0.00003005054,0.0001094656,0.00006505651,0.0003006956,0.0002381755],"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":[0.00002533485,0.000002371182,0.000008211769,0.0001008049,0.00004473398,0.000002033541,0.0004229187,0.9822525,0.01664397,0.0003050539,0.00006240933,0.0001296266],"study_design_scores_gemma":[0.0004192549,0.00002805565,0.000005936098,0.00006628488,0.00002263068,0.0000510699,0.0001201437,0.964137,0.03407533,0.000001148616,0.0009409537,0.0001322468],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007763546,0.0001408942,0.9909946,0.0005332795,0.0002221419,0.0002325237,0.00002521294,0.000073093,0.0000146932],"genre_scores_gemma":[0.9058625,0.000005165605,0.09374119,0.00008297244,0.0002013244,0.00001387782,0.000043548,0.00003957645,0.000009857273],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8980989,"threshold_uncertainty_score":0.4832112,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01452657941384164,"score_gpt":0.2048836583976732,"score_spread":0.1903570789838316,"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."}}