{"id":"W2518909758","doi":"10.1016/j.ifacol.2016.07.381","title":"Distributional Uncertainty Analysis in Transient Heterogeneous Multiscale Catalytic Flow Reactors","year":2016,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Transient (computer programming); Monte Carlo method; Kinetic Monte Carlo; Uncertainty analysis; Continuous reactor; Work (physics); Nuclear engineering; Statistical physics; Biological system; Environmental science; Mechanics; Computer science; Process engineering; Catalysis; Thermodynamics; Chemistry; Physics; Simulation; Mathematics; Engineering; Statistics","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":[],"consensus_categories":[],"category_scores_codex":[0.00009495497,0.0002223639,0.0003512279,0.0001826511,0.00003510723,0.00001337325,0.0001307169,0.0001062125,0.00007543927],"category_scores_gemma":[0.00005508882,0.0001717699,0.0001666184,0.0005317681,0.00003643022,0.0001454778,0.00001155702,0.00009271905,0.00002957049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004662696,"about_ca_system_score_gemma":0.00001621568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008678569,"about_ca_topic_score_gemma":0.001398108,"domain_scores_codex":[0.9987112,0.00003150578,0.0003851508,0.0003008946,0.0002178493,0.0003534358],"domain_scores_gemma":[0.9994155,0.0001030595,0.00004486219,0.0002730093,0.00004925049,0.000114357],"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":[0.00002327555,0.00003628725,0.002164188,0.00001298783,0.0002033726,0.00001821722,0.0001038835,0.9782873,0.01112532,0.000009770282,0.000001546956,0.008013825],"study_design_scores_gemma":[0.001315356,0.00002309884,0.00563021,0.00004513176,0.000135767,0.000009923543,0.00002548588,0.9912596,0.0009110339,0.00001329083,0.0003189902,0.0003121371],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4487678,0.0004153887,0.5478055,0.0004249195,0.0003100219,0.0003960204,0.001330264,0.0003701941,0.0001799681],"genre_scores_gemma":[0.9824685,0.00004441912,0.01672146,0.00001607728,0.00009165716,0.00003843971,0.0004847967,0.00002975119,0.0001049374],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5337007,"threshold_uncertainty_score":0.7004572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005273308731599977,"score_gpt":0.2045747827258786,"score_spread":0.1993014739942786,"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."}}