{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001082916,0.0002073043,0.0002386072,0.0005488349,0.0003069759,0.0006100159,0.0002673921,0.0005072129,0.003880965],"category_scores_gemma":[0.0004530304,0.0001744685,0.0001249328,0.0007334324,0.0002219945,0.0006264801,0.000365574,0.0006257458,0.00047204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005889342,"about_ca_system_score_gemma":0.0005450366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0130088,"about_ca_topic_score_gemma":0.02766657,"domain_scores_codex":[0.9999329,0.00000409466,0.000001550968,0.00002864478,0.00001757076,0.00001532768],"domain_scores_gemma":[0.9998692,0.0000281334,0.00002171989,0.00001295516,0.00004809958,0.00001983536],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009900389,0.0001717982,0.02467788,0.0004658447,0.000116754,0.0005064388,0.0004056316,0.03735239,0.7701822,0.006625586,0.02878983,0.1297156],"study_design_scores_gemma":[0.00006256342,0.0002331849,0.09640833,0.0001253578,0.0001514907,0.0008923909,0.000462141,0.4961269,0.3637357,0.005024012,0.03663411,0.0001439238],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6944495,0.001353031,0.2175536,0.003078971,0.0002421228,0.00009019556,0.008240065,0.00246607,0.07252648],"genre_scores_gemma":[0.9459754,0.0006774769,0.04143183,0.0002436564,0.00005443802,0.00005036739,0.002273292,0.0003066135,0.008986858],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0130088,"threshold_uncertainty_score":0.02586615,"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."}}