{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001597289,0.0001943373,0.0001543273,0.0001897258,0.0000378985,0.00006747498,0.0001231361,0.00006983757,0.00004949853],"category_scores_gemma":[0.000002296892,0.0001829407,0.00004139484,0.0001974046,0.00002393697,0.0002664455,0.00001632522,0.0003140954,0.00002018708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004132905,"about_ca_system_score_gemma":0.0000173297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001018257,"about_ca_topic_score_gemma":0.000001188487,"domain_scores_codex":[0.9991337,0.00001815392,0.0001645743,0.0002461155,0.0001976154,0.000239851],"domain_scores_gemma":[0.9995963,0.00007723409,0.00001159466,0.0002347084,0.0000136094,0.00006652902],"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":[0.00001021852,0.000002602617,0.000001711045,0.000124422,0.00005622897,0.00001712679,0.0006420526,0.9866314,0.002893727,0.005601408,0.003768827,0.0002503075],"study_design_scores_gemma":[0.0001725204,0.000006184926,0.00001010672,0.00005453589,0.00001688538,0.000004543102,0.00003068341,0.9942001,0.0002684398,0.0006080601,0.004393649,0.0002342745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01748061,0.0003443925,0.9715067,0.0004222991,0.0003176525,0.0001677211,0.000002306053,0.001188593,0.008569788],"genre_scores_gemma":[0.7923938,0.00002011103,0.2066052,0.0004563602,0.0001321415,0.00007094227,0.00002024118,0.0001187508,0.000182486],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7749132,"threshold_uncertainty_score":0.7460107,"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."}}