{"id":"W2972920323","doi":"10.1016/j.fuel.2019.116123","title":"Experimental and numerical analyses of apparent gas diffusion coefficient in gas shales","year":2019,"lang":"en","type":"article","venue":"Fuel","topic":"Hydrocarbon exploration and reservoir analysis","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Chengdu University of Technology; State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation; National Natural Science Foundation of China","keywords":"Knudsen diffusion; Diffusion; Methane; Effective diffusion coefficient; Grain boundary diffusion coefficient; Chemistry; Knudsen number; Molecular diffusion; Oil shale; Surface diffusion; Thermodynamics; Gaseous diffusion; Mineralogy; Analytical Chemistry (journal); Physical chemistry; Geology; Chromatography; Organic chemistry","routes":{"ca_aff":true,"ca_fund":false,"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":[0.00003666697,0.00007619051,0.0001688021,0.0001184569,0.000008497882,0.000009457659,0.00005148609,0.00003084683,0.0001483177],"category_scores_gemma":[0.000004628247,0.00006324023,0.00004269721,0.0001882604,0.00001670477,0.000038337,0.0000297024,0.00004885897,0.00002356012],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002350462,"about_ca_system_score_gemma":0.000003745342,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005874813,"about_ca_topic_score_gemma":0.000009615137,"domain_scores_codex":[0.9994866,0.00001623738,0.0001644053,0.0001051306,0.0001302358,0.00009736567],"domain_scores_gemma":[0.999806,0.00001433504,0.0000179456,0.0001087553,0.000008483303,0.00004451542],"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":[0.00002421895,0.0003046503,0.02477359,0.0001474681,0.00007159047,0.000007337781,0.002648576,0.2811687,0.6898237,0.000112171,0.0001602994,0.0007576577],"study_design_scores_gemma":[0.0004351966,0.00004624279,0.003612378,0.00002969291,0.00001244529,0.000001303454,0.001120675,0.9271176,0.06711773,0.00001850542,0.0003655676,0.0001226269],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.994878,0.0014337,0.001624324,0.00002555813,0.00005582342,0.00006461563,0.000001946914,0.00002968947,0.001886347],"genre_scores_gemma":[0.9997671,0.00008246247,0.00006770321,0.000009059717,0.00001123673,0.000005628353,0.00001093039,0.000008476683,0.0000374524],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6459489,"threshold_uncertainty_score":0.2578862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01966364801183451,"score_gpt":0.2725605375395935,"score_spread":0.252896889527759,"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."}}