{"id":"W4378009083","doi":"10.1016/j.fuel.2023.128755","title":"Numerical studies of hydrogen buoyant flow in storage aquifers","year":2023,"lang":"en","type":"article","venue":"Fuel","topic":"CO2 Sequestration and Geologic Interactions","field":"Environmental Science","cited_by":39,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Bureau of Economic Geology, University of Texas at Austin; Computer Modelling Group; University of Texas at Austin","keywords":"Hydrogen; Permeability (electromagnetism); Buoyancy; Chemistry; Anisotropy; Aquifer; Thermodynamics; Mechanics; Groundwater; Geology; Physics; Geotechnical engineering","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00009800831,0.00004094741,0.00007346837,0.00002892767,0.00002250036,0.000002299207,0.00005817329,0.00001842428,0.001347694],"category_scores_gemma":[0.00006733922,0.00003473065,0.0000232904,0.000258497,0.00006522994,0.00005339022,0.00005062958,0.00005044677,0.0009735936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004899933,"about_ca_system_score_gemma":0.00000421873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001800641,"about_ca_topic_score_gemma":0.0002604849,"domain_scores_codex":[0.9995744,0.00002463034,0.0001096643,0.00009789864,0.00009502603,0.00009836186],"domain_scores_gemma":[0.9998308,0.00004254109,0.00002332461,0.0000791472,0.000003546141,0.0000206428],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001122783,0.0006618648,0.3705741,0.00009830918,0.0001216457,0.0003417805,0.02518755,0.4392921,0.04494642,0.001333034,0.09917086,0.01816001],"study_design_scores_gemma":[0.0008967252,0.0003737044,0.7959939,0.00005167319,0.00002123145,0.00002299035,0.00672539,0.1017726,0.005498975,0.009359317,0.078785,0.0004985516],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9832445,0.00004034518,0.00004331925,0.001022043,0.0001697958,0.00007369989,0.000003567158,0.00004271733,0.01536002],"genre_scores_gemma":[0.9980035,0.00004206322,0.0001551176,0.00009323734,0.0000110795,0.000009854329,0.000003681232,0.000002229091,0.001679168],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4254197,"threshold_uncertainty_score":0.9998043,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0386703598462164,"score_gpt":0.308922866825215,"score_spread":0.2702525069789986,"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."}}