{"id":"W2950006358","doi":"10.1021/acs.energyfuels.9b01317","title":"Characterization of Shale Pore Size Distribution by NMR Considering the Influence of Shale Skeleton Signals","year":2019,"lang":"en","type":"article","venue":"Energy & Fuels","topic":"Hydrocarbon exploration and reservoir analysis","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"Geological Survey of Canada","funders":"Ministry of Science and Technology of the People's Republic of China; Ministry of Education of the People's Republic of China; Ministry of Human Resources and Social Security; National Natural Science Foundation of China","keywords":"Oil shale; Shale oil; Kerogen; Chemistry; Relaxometry; Carbon-13 NMR; NMR spectra database; Proton NMR; Mineralogy; Analytical Chemistry (journal); Spectral line; Geology; Organic chemistry; Spin echo; Source rock; Magnetic resonance imaging","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":[0.0002561693,0.0002439582,0.0001914403,0.0006972051,0.000199744,0.0002476911,0.0001532074,0.0002054247,0.0005166403],"category_scores_gemma":[0.0003360523,0.0001211979,0.0001158382,0.0002878195,0.0002967239,0.0004419209,0.0002554588,0.0002236843,0.0001062367],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001110936,"about_ca_system_score_gemma":0.000169151,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00195246,"about_ca_topic_score_gemma":0.003301396,"domain_scores_codex":[0.9998891,0.000009772609,0.000005565843,0.00003181077,0.00004342406,0.00002027718],"domain_scores_gemma":[0.9998125,0.00004085447,0.00003426533,0.000009613262,0.0000842757,0.00001856121],"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":[0.00003020155,0.000007309117,0.003551273,0.0000486362,0.000005402259,0.00007183989,0.00009631272,0.0002653078,0.9933867,0.00005218699,0.00002036187,0.00246449],"study_design_scores_gemma":[0.000008637106,0.0001836547,0.1255141,0.00001193529,0.00003552938,0.0004494828,0.0004793167,0.01183569,0.8598887,0.000153998,0.00140224,0.0000367604],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9944497,0.0002169742,0.004623099,0.00001019319,0.000003057269,0.00001092795,0.0001574574,0.00003608344,0.0004924608],"genre_scores_gemma":[0.9950296,0.0002649255,0.003957852,0.00001634582,0.000005238261,0.00002026609,0.0002149212,0.00001376763,0.0004770963],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00195246,"threshold_uncertainty_score":0.003882229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005039818362830315,"score_gpt":0.1899087102983516,"score_spread":0.1848688919355213,"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."}}