{"id":"W2585516984","doi":"10.1021/acs.iecr.6b03420","title":"Design of Bitumen Upgrading and Utility Plant through Integrated Optimization","year":2017,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Global Energy and Sustainability Research","field":"Energy","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates - Technology Futures","keywords":"Asphalt; Power station; Oil sands; Work (physics); Process engineering; Fossil fuel; Natural gas; Environmental science; Optimal design; Computer science; Waste management; Engineering; Mechanical engineering; Materials science","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":[0.001870092,0.0001744089,0.0002818519,0.00006559057,0.0003746078,0.000195543,0.0005950729,0.0003897427,0.0001709401],"category_scores_gemma":[0.005154683,0.0001681266,0.0000422809,0.0002685585,0.00035109,0.0002447571,0.0003200944,0.0009081499,0.000001672116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001841186,"about_ca_system_score_gemma":0.0002941191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00281095,"about_ca_topic_score_gemma":0.000003886814,"domain_scores_codex":[0.9980196,0.0001425513,0.000309068,0.0003751152,0.0005526722,0.0006009868],"domain_scores_gemma":[0.9983018,0.0004062066,0.00007074851,0.000689328,0.0003681437,0.0001637802],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006455631,0.0001391389,0.001153016,0.0003370908,0.0001484126,0.00006812001,0.0002865774,0.7863612,0.2000065,0.002468307,0.0004195939,0.007966456],"study_design_scores_gemma":[0.001553195,0.00009959129,0.0001084262,0.0002062802,0.00001246247,0.00001715808,0.0003413672,0.4527166,0.5379978,0.001214201,0.005434419,0.0002984665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9775193,0.0002822253,0.006456598,0.0007488538,0.0002700467,0.0008209074,0.00006911043,0.0002096333,0.01362336],"genre_scores_gemma":[0.9986225,0.00005487383,0.000479077,0.000001097187,0.0001722387,0.00003293071,0.00003314665,0.00002254375,0.0005816157],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3379913,"threshold_uncertainty_score":0.6856002,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1414728919031073,"score_gpt":0.3442528531940348,"score_spread":0.2027799612909275,"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."}}