{"id":"W2051681316","doi":"10.1007/s11242-007-9172-x","title":"Analysis of Pore Network in Three-dimensional (3D) Grain Bulks Using X-ray CT Images","year":2007,"lang":"en","type":"article","venue":"Transport in Porous Media","topic":"Hydrocarbon exploration and reservoir analysis","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Airflow; Hexahedron; Marching cubes; Materials science; Tomography; Geometry; Skeletonization; Path (computing); Porosity; Computed tomography; Volume (thermodynamics); Finite element method; Composite material; Computer science; Mathematics; Structural engineering; Artificial intelligence; Optics; Mechanical engineering; Physics; Engineering","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":[0.0001099721,0.0001939837,0.0001259823,0.0009495043,0.0002107114,0.000621147,0.0002072559,0.0003437531,0.00173206],"category_scores_gemma":[0.0002840561,0.0002413384,0.0001019356,0.0005169045,0.0003528064,0.0004198824,0.0001679089,0.0002323044,0.00014834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002545471,"about_ca_system_score_gemma":0.0003660872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004322283,"about_ca_topic_score_gemma":0.004038486,"domain_scores_codex":[0.9999684,0.000001655815,0.000002740609,0.000006423524,0.00001469819,0.000006151024],"domain_scores_gemma":[0.9998112,0.00008057801,0.00002834452,0.00001762921,0.00004428978,0.00001788011],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0004132447,0.0000846751,0.01644499,0.0002854785,0.0000298175,0.000769959,0.0003969969,0.04386533,0.9135206,0.00244909,0.0005310746,0.0212086],"study_design_scores_gemma":[0.00004597914,0.0001489798,0.1473989,0.00006316009,0.00007363629,0.002205449,0.0006425009,0.4100388,0.4343523,0.001486938,0.003450023,0.00009344352],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9544427,0.0002501084,0.04142474,0.00007340632,0.000006675571,0.00004496766,0.001085369,0.0005155319,0.002156444],"genre_scores_gemma":[0.9812982,0.0001457328,0.01734804,0.00001331188,0.000003123833,0.00001916438,0.0004312191,0.00004246899,0.0006987005],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004322283,"threshold_uncertainty_score":0.008594275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01356976191796437,"score_gpt":0.2341190962667685,"score_spread":0.2205493343488041,"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."}}