{"id":"W2946782756","doi":"10.1016/j.compchemeng.2019.05.010","title":"Integrated optimal design and scheduling for a bitumen upgrader facility","year":2019,"lang":"en","type":"article","venue":"Computers & Chemical Engineering","topic":"Process Optimization and Integration","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Scheduling (production processes); Profit (economics); Computer science; Electricity; Schedule; Mathematical optimization; Operations research; Engineering; Operations management; Economics","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":[0.00007623222,0.0001678356,0.0001739786,0.00006740243,0.0000160069,0.00005900277,0.00009785228,0.00009295449,0.00001166618],"category_scores_gemma":[0.00003694656,0.0001698097,0.00003940095,0.0001273678,0.00001129695,0.0001696344,0.0000222673,0.0001502763,0.000008052909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006004061,"about_ca_system_score_gemma":0.00001050002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.445928e-7,"about_ca_topic_score_gemma":1.217327e-8,"domain_scores_codex":[0.9993603,0.000003665759,0.0001742927,0.0001912677,0.00006538763,0.0002051224],"domain_scores_gemma":[0.9996757,0.00008374463,0.0000133742,0.0001002436,0.00004424662,0.00008268515],"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":[0.000007338665,0.000005212438,0.000008889637,0.0001132946,0.00002231212,1.938854e-7,0.00008897903,0.9089585,0.08826047,0.0001050843,0.000142937,0.002286818],"study_design_scores_gemma":[0.0003447286,0.00001615898,0.000004963963,0.00004763197,0.00000599172,0.000003415194,0.00001115133,0.9283183,0.07018077,0.00001119703,0.0008740281,0.0001817132],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1321045,0.0001416105,0.8668408,0.00002408003,0.0001970845,0.0002459458,0.000005008675,0.0004198937,0.00002113953],"genre_scores_gemma":[0.6222498,0.00000962705,0.3775834,0.00002509043,0.00003023136,0.00002952491,0.00004035026,0.00002104437,0.00001098549],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4901453,"threshold_uncertainty_score":0.6924639,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009677551543074312,"score_gpt":0.1910601758799057,"score_spread":0.1813826243368314,"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."}}