{"id":"W4297549817","doi":"10.1007/s11242-022-01857-6","title":"Poro-Mechanical Coupling for Flow Diagnostics","year":2022,"lang":"en","type":"article","venue":"Transport in Porous Media","topic":"Hydraulic Fracturing and Reservoir Analysis","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Energi Simulation","keywords":"Flow (mathematics); Mechanics; Hydrogeology; Reservoir simulation; Fluid dynamics; Coupling (piping); Fluid mechanics; Uncertainty quantification; Geomechanics; Porous medium; Reservoir engineering; Statistical physics; Computer science; Geology; Geotechnical engineering; Physics; Porosity; Engineering; Mechanical engineering; Petroleum engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007906706,0.0008326969,0.0006396537,0.0006553096,0.0005898401,0.001353109,0.001242683,0.001286553,0.003759598],"category_scores_gemma":[0.002940843,0.0005389839,0.0006749639,0.0003728059,0.0009629531,0.001274335,0.002240736,0.001334219,0.000511256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006000375,"about_ca_system_score_gemma":0.001119485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003206372,"about_ca_topic_score_gemma":0.001608429,"domain_scores_codex":[0.9995201,0.0001368411,0.00002768286,0.00007425378,0.0001885386,0.00005271783],"domain_scores_gemma":[0.9987715,0.000602531,0.0001469295,0.0002054541,0.0001903288,0.00008315525],"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":[0.00003501324,0.00006584208,0.001295478,0.00005767448,0.00002511553,0.00009522816,0.00006145696,0.9674589,0.007120051,0.0107156,0.0002625605,0.01280709],"study_design_scores_gemma":[0.000002762073,0.00001023555,0.00009841436,0.000003199875,0.000002192444,0.00001273956,0.000005389558,0.9969293,0.0009220268,0.001618664,0.0003902462,0.000004879864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07255033,0.0001365592,0.9192387,0.0002325916,0.00007765912,0.0001254232,0.0001574643,0.001120288,0.006360934],"genre_scores_gemma":[0.8621522,0.0001403744,0.1347453,0.0001130918,0.00004174808,0.0001965362,0.0002346573,0.0002834871,0.002092603],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003759598,"threshold_uncertainty_score":0.01257712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01037909643519452,"score_gpt":0.2126792466529723,"score_spread":0.2023001502177778,"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."}}