{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003350608,0.0001671404,0.0001696182,0.0001724857,0.00004155159,0.0001236423,0.0002483567,0.0002730054,0.0004747708],"category_scores_gemma":[0.0006774859,0.0001811996,0.0000302656,0.0006333062,0.00002715925,0.0002782766,0.00004643675,0.001087089,0.0000684915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002081536,"about_ca_system_score_gemma":0.00005077729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004638597,"about_ca_topic_score_gemma":7.736617e-7,"domain_scores_codex":[0.9986408,0.00001397417,0.0002977027,0.0002048639,0.000316062,0.0005265497],"domain_scores_gemma":[0.9993528,0.0001568058,0.00001419767,0.0001988629,0.0001288444,0.0001484645],"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":[0.000005262858,0.00001472848,0.0005750328,0.00005493354,0.00000879962,0.000005419472,0.00006104237,0.6918256,0.3037251,0.000005036291,0.002959476,0.0007595895],"study_design_scores_gemma":[0.0005586279,0.000008682871,0.0001081534,0.0001112842,9.998397e-7,0.000004037948,0.00006958438,0.7025588,0.2937605,0.000007107975,0.002658156,0.0001541239],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9855614,0.0004202425,0.001808295,0.0001959798,0.0003217566,0.0005327964,0.00001269726,0.0006625209,0.01048431],"genre_scores_gemma":[0.9985611,0.00002961905,0.0003043287,0.000003563599,0.0004133002,0.0001509152,0.00004926528,0.00004700792,0.0004408868],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01299972,"threshold_uncertainty_score":0.7389104,"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."}}