{"id":"W2894895528","doi":"10.1002/cjce.23354","title":"Application of nuclear magnetic resonance permeability models in tight reservoirs","year":2018,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"NMR spectroscopy and applications","field":"Physics and Astronomy","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Maersk Oil; Canadian Natural Resources Limited; University of Calgary","keywords":"Permeability (electromagnetism); Tight gas; Materials science; Geology; Chemistry; Petroleum engineering; Hydraulic fracturing","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.000132718,0.00005244172,0.0000969418,0.00004084307,0.00003103773,0.00001023835,0.0002276228,0.00002184013,0.00006537241],"category_scores_gemma":[0.000009188108,0.00004345023,0.00003843055,0.0001522576,0.00008395509,0.00005589024,0.000008477319,0.000174199,0.000002668213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005715947,"about_ca_system_score_gemma":0.00009624691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002071108,"about_ca_topic_score_gemma":0.0001629768,"domain_scores_codex":[0.9995361,0.000006227578,0.0001996778,0.00005834672,0.00006628095,0.0001333866],"domain_scores_gemma":[0.9995969,0.00002466151,0.0000553705,0.0001527831,0.00006485244,0.0001054452],"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":[0.00005468705,0.00009603349,0.01074552,0.00004571995,0.0000340605,0.000002292121,0.002944209,0.02067808,0.5286328,0.4233456,0.0008684609,0.01255252],"study_design_scores_gemma":[0.001151513,0.0001661429,0.0137519,0.000263027,0.00005588162,0.00001760067,0.0001714887,0.5940637,0.2640676,0.1013855,0.02439374,0.0005118595],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9957219,0.0001469005,0.002268102,0.0005581109,0.00002309191,0.000071517,0.00000783378,0.000002583326,0.001199905],"genre_scores_gemma":[0.9990429,3.750359e-7,0.0007651362,0.00001195399,0.000160966,0.000003362002,6.136544e-7,0.000007693446,0.000006977832],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5733856,"threshold_uncertainty_score":0.313091,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006023325101706889,"score_gpt":0.2275163832144514,"score_spread":0.2214930581127446,"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."}}