{"id":"W4220685980","doi":"10.1029/2021wr031454","title":"Generalizable Permeability Prediction of Digital Porous Media via a Novel Multi‐Scale 3D Convolutional Neural Network","year":2022,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Enhanced Oil Recovery Techniques","field":"Engineering","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Convolutional neural network; Computer science; Permeability (electromagnetism); Porous medium; Artificial intelligence; Deep learning; Characterization (materials science); Artificial neural network; Reservoir modeling; Machine learning; Porosity; Geology; Petroleum engineering; Geotechnical engineering; Materials science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009861885,0.0001558774,0.0002204635,0.0001955871,0.0002792289,0.00006012868,0.0004059874,0.00008385837,0.0004033817],"category_scores_gemma":[0.00004403279,0.0001393044,0.00008191444,0.0003612535,0.0002242164,0.0002215886,0.0004872728,0.0006478615,0.00001317455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003292064,"about_ca_system_score_gemma":0.00001681928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001553527,"about_ca_topic_score_gemma":0.0000342665,"domain_scores_codex":[0.9974566,0.0001512141,0.0003693885,0.0003243019,0.0009925844,0.0007059863],"domain_scores_gemma":[0.9992194,0.0001328051,0.00002460867,0.0003661123,0.0001469158,0.0001101375],"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":[0.0001739939,0.0002945763,0.03326392,0.0001696767,0.00007159769,0.00001234783,0.004351516,0.7966779,0.158353,0.00001233268,0.003379724,0.003239422],"study_design_scores_gemma":[0.0008225824,0.0003566135,0.01283424,0.00002608307,0.0000105001,0.00005921277,0.0002614892,0.9107646,0.03206536,0.0007002638,0.04176403,0.000335084],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9892532,0.0002784759,0.007780224,0.0000362219,0.000222891,0.0003380658,0.0003977694,0.000373247,0.001319857],"genre_scores_gemma":[0.9958663,0.0000144021,0.002780335,0.000007779198,0.0002152279,0.000235296,0.0002382308,0.0000531637,0.0005892548],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1262876,"threshold_uncertainty_score":0.5680667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03926734745785691,"score_gpt":0.2694914709029727,"score_spread":0.2302241234451158,"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."}}