{"id":"W2022479271","doi":"10.1115/fedsm-icnmm2010-30308","title":"Use of Micro-CT Images to Reconstruct Porous Media for Pore Network Model","year":2010,"lang":"en","type":"article","venue":"","topic":"Enhanced Oil Recovery Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Porosity; Porous medium; Network model; Characterisation of pore space in soil; Multiphase flow; Flow (mathematics); Sample (material); Core sample; Materials science; Geology; Core (optical fiber); Computer science; Geometry; Mechanics; Artificial intelligence; Geotechnical engineering; Mathematics; Physics; Composite material","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":[0.0004405813,0.0006313625,0.0002509945,0.001465082,0.0001447243,0.0008066227,0.0005129693,0.0006681026,0.001653982],"category_scores_gemma":[0.001061307,0.0003974877,0.00045519,0.0007316704,0.0002714159,0.0008539993,0.0003273932,0.0004804831,0.0003971997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003559031,"about_ca_system_score_gemma":0.0005010073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00144239,"about_ca_topic_score_gemma":0.001732308,"domain_scores_codex":[0.9998438,0.00002241086,0.00001210454,0.00004240825,0.00006371237,0.00001540001],"domain_scores_gemma":[0.9996699,0.0001244199,0.00005365128,0.00005667726,0.00007676262,0.0000185722],"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":[0.0002787696,0.0001356527,0.007084991,0.0006203026,0.00008510689,0.001081289,0.000241034,0.5022241,0.3633873,0.0081112,0.001142363,0.1156079],"study_design_scores_gemma":[0.000006949994,0.0000314272,0.00241529,0.00001704935,0.00001897322,0.0003516717,0.00005606866,0.9506419,0.04383743,0.001443822,0.001156226,0.00002315004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1142134,0.0004967315,0.8803644,0.0001263412,0.0000276345,0.000137611,0.0008254435,0.001808025,0.002000437],"genre_scores_gemma":[0.559033,0.0006576024,0.4381343,0.0000437161,0.00001315178,0.0001090987,0.0008357213,0.0001876896,0.0009857552],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001653982,"threshold_uncertainty_score":0.005533099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01594600524142707,"score_gpt":0.2311517153844605,"score_spread":0.2152057101430335,"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."}}