{"id":"W4401920107","doi":"10.1002/ail2.99","title":"History Matching Reservoir Models With Many Objective Bayesian Optimization","year":2024,"lang":"en","type":"article","venue":"Applied AI Letters","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Science and Technology Facilities Council; Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; Energi Simulation","keywords":"Matching (statistics); Computer science; Bayesian probability; Bayesian optimization; Machine learning; Artificial intelligence; Mathematics; Statistics","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":[0.002251302,0.0007109775,0.001222697,0.00074882,0.0005379918,0.001343661,0.001810799,0.001369679,0.002999139],"category_scores_gemma":[0.006476054,0.000918734,0.0009537906,0.0009085473,0.0009767532,0.001543075,0.001682316,0.001567328,0.0004184551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001507815,"about_ca_system_score_gemma":0.001760098,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02260887,"about_ca_topic_score_gemma":0.01383698,"domain_scores_codex":[0.9993103,0.0002961089,0.00003387062,0.0001083234,0.000167288,0.00008425136],"domain_scores_gemma":[0.9971684,0.001819043,0.0002616956,0.0002094777,0.0004001246,0.0001413439],"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":[0.00001905146,0.00001503982,0.0003913433,0.00000725569,0.00001304466,0.00001004499,0.000007312729,0.9936056,0.0001176054,0.002496784,0.0001425967,0.003174394],"study_design_scores_gemma":[0.000002656829,0.000002685001,0.00003061153,9.612198e-7,8.711884e-7,9.79996e-7,0.000001374955,0.9989038,0.0000672224,0.0009277173,0.00005978787,0.000001353744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04800417,0.00014228,0.9470991,0.0002992872,0.00003877512,0.00006918023,0.000186677,0.0005704785,0.003590102],"genre_scores_gemma":[0.7538307,0.0001263059,0.2415038,0.0002041012,0.00005813663,0.0001940938,0.0004129784,0.0002952646,0.003374561],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02260887,"threshold_uncertainty_score":0.04495454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01033560644801596,"score_gpt":0.2165084265800583,"score_spread":0.2061728201320423,"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."}}