{"id":"W4408728842","doi":"10.1190/geo2024-0402.1","title":"Geophysical control of geologic carbon storage using deep reinforcement learning: Sensitivity to multigeophysical noise and to the uncertainty of digital twins","year":2025,"lang":"en","type":"article","venue":"Geophysics","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Geophysics; Exploration geophysics; Sensitivity (control systems); Noise (video); Geology; Computer science; Artificial intelligence; Engineering","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":[0.001587749,0.0007876738,0.0007323766,0.00026114,0.0003041533,0.000640314,0.0009171042,0.0009672684,0.0008896154],"category_scores_gemma":[0.006612699,0.0003798083,0.0003381673,0.0001707829,0.001404836,0.0007246251,0.0008849212,0.001374886,0.0001043969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001319468,"about_ca_system_score_gemma":0.001396167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01390022,"about_ca_topic_score_gemma":0.008604053,"domain_scores_codex":[0.9996767,0.0001147819,0.00001379659,0.00007031443,0.00004574819,0.00007863255],"domain_scores_gemma":[0.9963566,0.002462484,0.0004620548,0.0001944667,0.0003238129,0.0002007087],"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":[0.00004082408,0.00003434593,0.001201096,0.000009689544,0.00001177051,0.00001917811,0.000009975142,0.9949137,0.0003953451,0.0005898362,0.00008395127,0.002690249],"study_design_scores_gemma":[0.000004156194,0.000009613454,0.0000752178,0.000001042348,0.000001266003,0.000001087775,0.000001333965,0.999516,0.0001048731,0.0002726897,0.00001152481,0.000001202203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8240792,0.0002413981,0.1709697,0.001100398,0.00005457259,0.00005428695,0.00008997671,0.0004385301,0.002971952],"genre_scores_gemma":[0.9958802,0.00001676154,0.003703384,0.00005867287,0.000005002895,0.00001451551,0.00002253661,0.000006160689,0.0002926359],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01390022,"threshold_uncertainty_score":0.02763861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01047895480925025,"score_gpt":0.2486565618625978,"score_spread":0.2381776070533476,"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."}}