{"id":"W2598253748","doi":"10.1007/s10596-017-9635-2","title":"Modeling core-scale permeability anisotropy in highly bioturbated “tight oil” reservoir rocks","year":2017,"lang":"en","type":"article","venue":"Computational Geosciences","topic":"Advanced Mathematical Modeling in Engineering","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Alberta Innovates - Technology Futures","keywords":"Permeability (electromagnetism); Anisotropy; Hydrogeology; Geology; Relative permeability; Diagenesis; Scaling; Core sample; Reservoir simulation; Petrology; Fluid dynamics; Mineralogy; Geotechnical engineering; Geometry; Core (optical fiber); Mechanics; Petroleum engineering; Materials science; Porosity; Mathematics; Chemistry; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002084331,0.00033831,0.0003983173,0.0003701014,0.0003996799,0.00100768,0.000827484,0.001168031,0.0008458174],"category_scores_gemma":[0.00158891,0.0003953986,0.0003036185,0.0004030519,0.001210048,0.0009596234,0.0006787796,0.0006077277,0.00008097359],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001007494,"about_ca_system_score_gemma":0.001360204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03736631,"about_ca_topic_score_gemma":0.02150984,"domain_scores_codex":[0.9999206,0.00001416907,0.000004844278,0.00001725744,0.00001255121,0.00003051875],"domain_scores_gemma":[0.9995064,0.00023633,0.00009314902,0.00003796216,0.00005279056,0.00007344721],"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":[0.00006276825,0.00005330477,0.003053092,0.00001304091,0.00001045868,0.00008311668,0.00002731831,0.9915847,0.002551934,0.001623247,0.00006816237,0.0008689268],"study_design_scores_gemma":[0.000007109219,0.000006919948,0.0005669116,0.000001210962,0.000002589137,0.000005860637,0.00001492114,0.99879,0.0003453307,0.0002217455,0.00003400928,0.000003401701],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9905527,0.00006874758,0.005875768,0.0001802158,0.00001188982,0.000009780896,0.000072416,0.00008573817,0.003142736],"genre_scores_gemma":[0.998612,0.00003096359,0.0008569803,0.00001038229,0.000003639334,0.000003682947,0.00002741499,0.00001307898,0.0004417387],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03736631,"threshold_uncertainty_score":0.07429761,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04091675216656948,"score_gpt":0.2958035365791113,"score_spread":0.2548867844125418,"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."}}