{"id":"W7116059125","doi":"10.1016/j.jgsce.2025.205826","title":"Dynamic imaging of CO <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si90.svg\" display=\"inline\" id=\"d1e563\"> <mml:msub> <mml:mrow/> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msub> </mml:math> plume migration from sparse monitoring data using neural network models","year":2025,"lang":"en","type":"article","venue":"Gas Science and Engineering","topic":"CO2 Sequestration and Geologic Interactions","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Energi Simulation; University of Southern California","keywords":"Plume; Artificial neural network; Data acquisition; Dynamic data; Adaptability; Limit (mathematics); Iterative reconstruction; Tracking (education)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006573843,0.0002174953,0.0001101941,0.0001271828,0.0005668473,0.0003790939,0.0006849489,0.0001822891,0.002618487],"category_scores_gemma":[0.0002204245,0.0003248225,0.000122587,0.0005472286,0.0004486853,0.00175121,0.0007017649,0.0003600872,0.00006620019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002427556,"about_ca_system_score_gemma":0.0001630263,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001571591,"about_ca_topic_score_gemma":0.0006137823,"domain_scores_codex":[0.9975935,0.00003445712,0.000463439,0.0006285637,0.0006393098,0.0006406901],"domain_scores_gemma":[0.998674,0.000185076,0.0002538908,0.0006648595,0.0000294101,0.0001928115],"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":[0.00008785054,0.00007124507,0.0004442254,0.0001283932,0.00009036429,0.0001076412,0.001084779,0.8024242,0.05056015,0.1377257,0.003932894,0.003342578],"study_design_scores_gemma":[0.0001730104,0.000058097,0.0008895088,0.0002285866,0.00008776945,0.00007955237,0.0004028743,0.9178444,0.07965183,0.00004029849,0.000292812,0.0002512663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9779947,0.000204469,0.007506595,0.0002935581,0.001111189,0.00002610967,0.0000547425,0.00008064354,0.01272795],"genre_scores_gemma":[0.9935685,0.0001729432,0.00557288,0.0001647916,0.0002307752,0.00004590858,0.0001591546,0.0000405312,0.00004456236],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1376854,"threshold_uncertainty_score":0.9999204,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02103048661847565,"score_gpt":0.2530351894781194,"score_spread":0.2320047028596437,"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."}}