{"id":"W2326792531","doi":"10.1021/ie3011963","title":"Multiple Optima in Gasoline Blend Planning","year":2013,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Process Optimization and Integration","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gasoline; Solver; Computer science; Time horizon; Refinery; Production (economics); Range (aeronautics); Nonlinear programming; Total cost; Volume (thermodynamics); Mathematical optimization; Production planning; Nonlinear system; Mathematics; Engineering; Waste 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001774256,0.001619127,0.001776279,0.00134004,0.0006505108,0.001425417,0.00124929,0.001486882,0.004636231],"category_scores_gemma":[0.004165898,0.001430205,0.00122102,0.001557198,0.001367646,0.001406664,0.001242052,0.001605056,0.0004641294],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001578321,"about_ca_system_score_gemma":0.001563185,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006680404,"about_ca_topic_score_gemma":0.008019361,"domain_scores_codex":[0.9993491,0.0002566602,0.00003375913,0.0001582872,0.0001194246,0.00008294364],"domain_scores_gemma":[0.9983265,0.001354682,0.0001257585,0.00004109285,0.00009768252,0.00005428934],"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":[0.00005735888,0.00003141212,0.00030498,0.000100597,0.00003726278,0.0000590853,0.00004450484,0.9760976,0.0002810154,0.006890993,0.0005051381,0.01559011],"study_design_scores_gemma":[0.00001869854,0.00002851774,0.00006186902,0.00002079567,0.00001534503,0.00001334373,0.00002251619,0.9916558,0.0003209179,0.007115321,0.0007196098,0.000007229183],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0502291,0.00223789,0.9331082,0.0004822207,0.00006676996,0.0002565444,0.0002308104,0.0005880508,0.01280042],"genre_scores_gemma":[0.4040245,0.0008823139,0.5867015,0.0001941844,0.00004924077,0.0004647539,0.0003588317,0.0002620649,0.007062625],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006680404,"threshold_uncertainty_score":0.01550972,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08620377477825937,"score_gpt":0.3135655970656955,"score_spread":0.2273618222874361,"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."}}