{"id":"W4388819120","doi":"10.1016/j.jnnfm.2023.105155","title":"A network model for gas invasion into porous media filled with yield-stress fluid","year":2023,"lang":"en","type":"article","venue":"Journal of Non-Newtonian Fluid Mechanics","topic":"Enhanced Oil Recovery Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Institute for Oil Sands Innovation, University of Alberta; Canada's Oil Sands Innovation Alliance; Natural Sciences and Engineering Research Council of Canada; University of British Columbia","keywords":"Porous medium; Inflow; Porosity; Mechanics; Network model; Yield (engineering); Materials science; Fluid dynamics; Flow (mathematics); Volume (thermodynamics); Volume fraction; Stress (linguistics); Composite material; Physics; Thermodynamics; Computer science","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":[0.0004476989,0.001099927,0.001416304,0.001141384,0.001270987,0.001841066,0.002624834,0.004986945,0.005012146],"category_scores_gemma":[0.001758561,0.0008850544,0.001133845,0.000958012,0.002190626,0.003369156,0.001767057,0.001497774,0.0005854766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002165242,"about_ca_system_score_gemma":0.001507487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02273626,"about_ca_topic_score_gemma":0.01105394,"domain_scores_codex":[0.9997956,0.00005348891,0.000008870786,0.00005343105,0.00004586696,0.00004277554],"domain_scores_gemma":[0.9993281,0.0003584943,0.00009619813,0.00002665452,0.00008746937,0.0001030315],"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":[0.00003434716,0.00004323464,0.0002659146,0.00004222884,0.00001549446,0.000118532,0.00003810949,0.9575172,0.00178893,0.03885985,0.0004307184,0.000845516],"study_design_scores_gemma":[0.000009143597,0.00000876564,0.00004416057,0.000002600285,0.000004223215,0.00001126773,0.00001056568,0.996729,0.0001075905,0.00284755,0.0002182819,0.000006766053],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3907059,0.002064286,0.5186443,0.004128052,0.0006142953,0.0002554934,0.001572628,0.0005086358,0.0815063],"genre_scores_gemma":[0.9183072,0.001266496,0.01888076,0.0003170763,0.0001214171,0.0003052517,0.0003512955,0.0001555056,0.06029494],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02273626,"threshold_uncertainty_score":0.0452078,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01684400838822658,"score_gpt":0.2291300320885862,"score_spread":0.2122860237003596,"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."}}