{"id":"W4413880994","doi":"10.1144/geoenergy2025-028","title":"From outcrop observations to dynamic simulations: an efficient workflow for generating ensembles of geologically plausible fracture networks and assessing their impact on flow and transport","year":2025,"lang":"en","type":"article","venue":"Geoenergy","topic":"Groundwater flow and contamination studies","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Energi Simulation","keywords":"Workflow; Outcrop; Fracture (geology); Flow (mathematics); Geology; Computer science; Petrology; Mechanics; Geotechnical engineering; Geomorphology; Physics; Database","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001099065,0.0001471166,0.0001938469,0.00004165283,0.0002910414,0.00005022779,0.00007267081,0.000072284,0.00003273503],"category_scores_gemma":[0.00002108728,0.0001033387,0.00003763934,0.0001510655,0.00005210092,0.0001023399,0.00004808301,0.00005434101,2.529206e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004669572,"about_ca_system_score_gemma":0.000009329541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005426235,"about_ca_topic_score_gemma":0.001522201,"domain_scores_codex":[0.9991994,0.00003392958,0.0002082913,0.0002916139,0.00009154815,0.0001752033],"domain_scores_gemma":[0.9995337,0.0002114359,0.00005481323,0.0001210733,0.00002509347,0.00005385495],"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":[0.00002251971,0.00004130372,0.05251126,0.000002955434,0.00002339756,3.289884e-7,0.0003638245,0.9126449,0.003005836,0.00003199868,0.00001846308,0.03133324],"study_design_scores_gemma":[0.0001614799,0.00005112729,0.4636995,0.00002232308,0.00001501684,1.397531e-7,0.0001052242,0.5353402,0.0001503076,0.0001398435,0.000245305,0.00006943272],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6524951,0.00007161581,0.3470333,0.0001929301,0.00003532031,0.0001173052,0.00002731508,0.00001368946,0.00001339682],"genre_scores_gemma":[0.9869228,0.000008377632,0.01219621,0.0005605824,0.00002419656,0.00002688374,0.00009421702,0.000006884927,0.000159863],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4111883,"threshold_uncertainty_score":0.4214029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01591551955043776,"score_gpt":0.2701827797155832,"score_spread":0.2542672601651455,"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."}}