{"id":"W4414121118","doi":"10.1016/j.jcp.2025.114337","title":"An <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si24.svg\"> <mml:mrow> <mml:mi>r</mml:mi> <mml:mi>p</mml:mi> </mml:mrow> </mml:math> -adaptive method for accurate resolution of shock-dominated viscous flow based on implicit shock tracking","year":2025,"lang":"lv","type":"article","venue":"Journal of Computational Physics","topic":"Computational Fluid Dynamics and Aerodynamics","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Air Force Office of Scientific Research; Division of Chemical, Bioengineering, Environmental, and Transport Systems; Office of Naval Research; National Science Foundation","keywords":"Tracking (education); Shock (circulatory); Viscous flow; Flow (mathematics); Resolution (logic); Viscous liquid","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":[0.0005025261,0.0004533398,0.0003418356,0.0005174226,0.0003700668,0.0008190395,0.001414075,0.0007775971,0.01428174],"category_scores_gemma":[0.001072433,0.0002837586,0.0005909661,0.0006618173,0.0003940713,0.0007911733,0.001133382,0.001363609,0.007775966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004373874,"about_ca_system_score_gemma":0.0009660895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001981152,"about_ca_topic_score_gemma":0.002671668,"domain_scores_codex":[0.9996675,0.00005464633,0.00002341711,0.00005245958,0.0001851257,0.00001684561],"domain_scores_gemma":[0.9996532,0.00008530645,0.00003908814,0.0001064843,0.00009195281,0.00002390053],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001150805,0.000166817,0.001035951,0.0004382749,0.00003413895,0.0002376343,0.0002722911,0.1365122,0.1025047,0.1644413,0.04792999,0.5463116],"study_design_scores_gemma":[0.0000290913,0.00004468775,0.000434749,0.00004877636,0.000008436038,0.0001701924,0.00002558036,0.8184265,0.02477319,0.01392749,0.1420741,0.00003722772],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001824058,0.00007032333,0.9877115,0.0001570907,0.00008303461,0.00005531351,0.0001599789,0.00115078,0.008787866],"genre_scores_gemma":[0.02926071,0.0003140071,0.9502966,0.0001518946,0.00007487127,0.0002066722,0.0006226086,0.001019595,0.0180531],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01428174,"threshold_uncertainty_score":0.04777724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0153284180236097,"score_gpt":0.268411742343909,"score_spread":0.2530833243202993,"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."}}