{"id":"W2999832611","doi":"10.1021/acs.iecr.9b06295","title":"Modeling the Hydrocracking Process with Deep Neural Networks","year":2020,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Petroleum Processing and Analysis","field":"Chemistry","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"State Administration of Foreign Experts Affairs; Ministry of Education of the People's Republic of China; National Natural Science Foundation of China","keywords":"Computer science; Process (computing); Convolutional neural network; Refinery; Artificial neural network; Deep learning; Artificial intelligence; Process modeling; Data mining; Machine learning; Work in process; Engineering","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":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0004543986,0.0002754036,0.0002875758,0.00003394788,0.0003633293,0.0003169938,0.0008405267,0.0002925633,0.00017918],"category_scores_gemma":[0.0006211437,0.000204074,0.00009411924,0.0009719148,0.0000947413,0.0001371659,0.0001435085,0.002747859,0.000004467137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009326941,"about_ca_system_score_gemma":0.0001197768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002919701,"about_ca_topic_score_gemma":8.730775e-7,"domain_scores_codex":[0.997433,0.00002119302,0.0003138005,0.0005270153,0.0008816565,0.0008233342],"domain_scores_gemma":[0.9988705,0.0001732532,0.00005404226,0.0003662191,0.0002149203,0.0003210582],"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":[0.00007721058,0.00001512353,0.0002062938,0.00014014,0.00007318959,0.00003343158,0.0001548436,0.8516114,0.144531,9.176475e-7,0.00002212192,0.003134314],"study_design_scores_gemma":[0.0005044998,0.00001258392,3.078737e-7,0.0001136739,0.00003387957,0.00001759119,0.0004293908,0.91164,0.08661894,0.000001903512,0.000397619,0.0002295489],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9925368,0.0004843378,0.001857593,0.001607222,0.00002788953,0.00002935028,0.000004686937,0.0003321712,0.003119958],"genre_scores_gemma":[0.9970512,0.000008365499,0.00001085149,0.00002722624,0.002466996,0.00004897869,0.00002426646,0.00006534042,0.0002967834],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06002862,"threshold_uncertainty_score":0.9995528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07625411212808064,"score_gpt":0.3059973561608481,"score_spread":0.2297432440327675,"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."}}