{"id":"W1851662160","doi":"10.1002/aic.15086","title":"Hybrid model for optimization of crude oil distillation units","year":2015,"lang":"en","type":"article","venue":"AIChE Journal","topic":"Process Optimization and Integration","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Ontario Research Foundation; McMaster University","keywords":"Distillation; Boiling point; Vacuum distillation; Process engineering; Heat exchanger; Mean squared error; Fractionating column; Boiling; Petroleum engineering; Engineering; Chemistry; Mathematics; Mechanical engineering; Chromatography; Statistics","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":[0.0001650089,0.00006526546,0.00008478334,0.00007705762,0.00003441803,0.00003341382,0.00006120114,0.00003479158,0.00001251199],"category_scores_gemma":[0.0001980305,0.00006016522,0.00002294667,0.0001038423,0.000008589272,0.0003552919,0.000004265397,0.00007467629,0.000001378344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005721297,"about_ca_system_score_gemma":0.00007123471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.918578e-7,"about_ca_topic_score_gemma":0.0000011813,"domain_scores_codex":[0.9995248,0.000008673132,0.000220817,0.00004329258,0.0001194131,0.00008301676],"domain_scores_gemma":[0.9992105,0.00001282051,0.00007458788,0.00005291122,0.0005766805,0.00007249501],"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":[0.00001331587,0.00000615806,0.00001693831,0.00001570551,0.00000960971,1.010436e-7,0.0002244675,0.991433,0.0002367824,0.0002184136,0.005137147,0.002688335],"study_design_scores_gemma":[0.0003940164,0.00002286143,0.00000408607,0.00002004978,0.00001382722,0.00001165771,0.00004173711,0.9955402,0.002904037,0.0005205685,0.0004609942,0.00006598309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006597468,0.0001604345,0.9905174,0.00007209894,0.0001804307,0.00003127976,0.00001111782,0.00004681526,0.002382906],"genre_scores_gemma":[0.9332545,0.0002069828,0.06588407,0.00003622561,0.0001065924,0.00000499818,0.00005620839,0.00002144695,0.0004289225],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9266571,"threshold_uncertainty_score":0.2453466,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04124078487906657,"score_gpt":0.2487570674018649,"score_spread":0.2075162825227984,"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."}}