{"id":"W2114901473","doi":"10.1016/j.petlm.2015.10.005","title":"Quantitative prediction of residual wetting film generated in mobilizing a two-phase liquid in a capillary model","year":2015,"lang":"en","type":"article","venue":"Petroleum","topic":"Enhanced Oil Recovery Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Capillary action; Residual oil; Capillary number; Porous medium; Wetting; Materials science; Viscosity; Residual; Two-phase flow; Reynolds number; Mechanics; Volume of fluid method; Weber number; Geotechnical engineering; Penetration (warfare); Petroleum engineering; Composite material; Flow (mathematics); Porosity; Geology; Engineering; Physics; Computer science; Turbulence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001837143,0.0002879927,0.0001706188,0.0003353041,0.0001222825,0.0002876557,0.0003142809,0.000422492,0.0005662439],"category_scores_gemma":[0.0006032179,0.0000918708,0.0002164671,0.0001718143,0.0002500961,0.0003437456,0.0001674784,0.0002039564,0.00008438469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002867928,"about_ca_system_score_gemma":0.0002419203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002764883,"about_ca_topic_score_gemma":0.001216689,"domain_scores_codex":[0.9999402,0.000008724738,0.00000280474,0.00001168579,0.00002536663,0.00001118184],"domain_scores_gemma":[0.9997845,0.0001196527,0.00002859877,0.00001472718,0.00004183913,0.00001063318],"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":[0.0001569671,0.00004843882,0.001987937,0.0001547223,0.00001188745,0.0003230825,0.00006650622,0.6337045,0.3435923,0.005263873,0.0001969449,0.01449279],"study_design_scores_gemma":[0.000002936128,0.00003700658,0.0003475978,0.000002586398,0.000002597871,0.00001608314,0.00000684115,0.9743245,0.02499807,0.0001497659,0.0001072012,0.000004693606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8285361,0.0004426182,0.1652084,0.00008266599,0.00002385541,0.0000376215,0.0001442986,0.0004215203,0.005102958],"genre_scores_gemma":[0.990148,0.0001257542,0.008824192,0.000003749962,0.000001982171,0.00001208686,0.00004443491,0.000014608,0.0008251905],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002764883,"threshold_uncertainty_score":0.005497575,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03820478765671537,"score_gpt":0.2962406100104189,"score_spread":0.2580358223537035,"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."}}