{"id":"W2605437439","doi":"10.1016/j.fuel.2017.04.057","title":"Liquid permeability of organic nanopores in shale: Calculation and analysis","year":2017,"lang":"en","type":"article","venue":"Fuel","topic":"Hydrocarbon exploration and reservoir analysis","field":"Engineering","cited_by":83,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"National Key Research and Development Program of China; National Science and Technology Major Project; National Natural Science Foundation of China","keywords":"Kerogen; Oil shale; Nanopore; Permeability (electromagnetism); Adsorption; Slip (aerodynamics); Materials science; Mineralogy; Mechanics; Geology; Chemical physics; Chemistry; Thermodynamics; Nanotechnology; Source rock; Organic chemistry; Membrane; Physics","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":[0.0001626303,0.0001964479,0.0002514259,0.0003652667,0.0003662706,0.0003438744,0.0004938332,0.0005846926,0.0007600217],"category_scores_gemma":[0.0004572965,0.00019156,0.0002897045,0.0003515068,0.0004090573,0.0006774473,0.0002575399,0.0002711913,0.0001198509],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005545486,"about_ca_system_score_gemma":0.0006713507,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009949496,"about_ca_topic_score_gemma":0.007883864,"domain_scores_codex":[0.9999572,0.000006281208,0.000002434204,0.00000688003,0.00001735637,0.000009872817],"domain_scores_gemma":[0.9997781,0.0001405658,0.00001731721,0.00001558349,0.00003615742,0.00001239612],"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":[0.00009566108,0.00008316991,0.00474966,0.0001436077,0.00002190221,0.0002334028,0.0000874871,0.9379153,0.03893191,0.006627185,0.0002295041,0.01088122],"study_design_scores_gemma":[0.000005885501,0.0000144625,0.0007982137,0.000003239847,0.000004125252,0.00001761287,0.00002131373,0.9911491,0.00735618,0.0004187024,0.0002062568,0.00000494858],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9287487,0.0003439006,0.06309139,0.0001156474,0.00001059031,0.00003208289,0.0002748077,0.0002176256,0.007165211],"genre_scores_gemma":[0.9943106,0.0001375323,0.004247538,0.000008934331,0.000002473591,0.00001892661,0.00004873094,0.00001716523,0.001208093],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009949496,"threshold_uncertainty_score":0.0197832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01207506966086577,"score_gpt":0.2425261050378749,"score_spread":0.2304510353770091,"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."}}