{"id":"W2343004437","doi":"10.1149/ma2016-01/45/2187","title":"(Invited) Numerical Simulations of Mass Transport Using Separation of Variables:  an Old Method Rejuvenated with Symbolic Algebra Software","year":2016,"lang":"en","type":"article","venue":"ECS Meeting Abstracts","topic":"NMR spectroscopy and applications","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Solver; Series (stratigraphy); Separation of variables; Symbolic computation; Eigenvalues and eigenvectors; Computer science; Applied mathematics; Partial differential equation; Algorithm; Mathematics; Mathematical optimization; Mathematical analysis; Physics","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":[0.0005930394,0.0004619314,0.0004139217,0.0004323048,0.0005065314,0.001255673,0.0009380637,0.0009297273,0.01561263],"category_scores_gemma":[0.001490563,0.0002664235,0.0005878942,0.0006045336,0.0007418275,0.00151111,0.0009918815,0.00103808,0.007807688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004594568,"about_ca_system_score_gemma":0.000548125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009911748,"about_ca_topic_score_gemma":0.0007373421,"domain_scores_codex":[0.9996412,0.00006699564,0.00001763428,0.00005750099,0.0001933476,0.00002336764],"domain_scores_gemma":[0.9996941,0.0000889801,0.00002436588,0.00006145897,0.0001066876,0.0000244549],"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":[0.0001779181,0.00006607569,0.001265468,0.0005635572,0.00008998909,0.0003786139,0.0005214941,0.234947,0.03791459,0.3043749,0.07701486,0.3426855],"study_design_scores_gemma":[0.00005639601,0.00005440408,0.0003926542,0.00007151452,0.00003006425,0.0002387376,0.0000417195,0.7069762,0.02029167,0.06442159,0.2073777,0.00004741562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005611623,0.0005659075,0.9652247,0.0007639087,0.001126219,0.00003854767,0.0002203134,0.002997255,0.02345146],"genre_scores_gemma":[0.2062799,0.00222005,0.721567,0.0006316922,0.0008371898,0.0004412798,0.0006159508,0.002048717,0.06535811],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01561263,"threshold_uncertainty_score":0.05222946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01581032711115549,"score_gpt":0.3324717286484546,"score_spread":0.3166614015372992,"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."}}