{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003289265,0.0001674554,0.0001573014,0.0002871474,0.0004057778,0.0002855939,0.0003508163,0.0002568746,0.00136747],"category_scores_gemma":[0.0009556513,0.0001570714,0.0001183413,0.0003030214,0.0006471555,0.0005423072,0.0002163424,0.0003268325,0.0001276447],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004801487,"about_ca_system_score_gemma":0.0002465279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002714731,"about_ca_topic_score_gemma":0.002755697,"domain_scores_codex":[0.999897,0.00001380444,0.000007681077,0.0000189424,0.00004805488,0.00001459101],"domain_scores_gemma":[0.9992195,0.0004341891,0.00006959189,0.00006312163,0.0001845076,0.00002905826],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005441786,0.0001339065,0.005942283,0.00008336222,0.000006570711,0.00006996444,0.0002284047,0.01235984,0.9737188,0.001513891,0.00008942922,0.005309349],"study_design_scores_gemma":[0.00002266191,0.0002183636,0.01479401,0.000005164757,0.000008033482,0.00006754541,0.00015861,0.05894415,0.9250951,0.0001583499,0.0005084763,0.0000195002],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9979624,0.00006830837,0.001314015,0.00001191672,0.000002665781,0.000002664531,0.00006223256,0.00001902741,0.0005569058],"genre_scores_gemma":[0.9988956,0.00004396408,0.0006523852,0.000001304953,9.236678e-7,0.000002928775,0.00004137593,0.000003594846,0.0003578832],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002714731,"threshold_uncertainty_score":0.005397916,"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."}}