{"id":"W77325401","doi":"10.4208/cicp.2009.v5.p779","title":"Accuracy Enhancement Using Spectral Postprocessing for Differential Equations and Integral Equations","year":2009,"lang":"en","type":"article","venue":"Communications in Computational Physics","topic":"Numerical methods for differential equations","field":"Mathematics","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Spectral method; Numerical analysis; Order of accuracy; Symplectic geometry; Mathematics; Applied mathematics; Differential equation; Hamiltonian (control theory); Ode; Gauss; Integral equation; Algorithm; Gauss–Seidel method; Computer science; Mathematical analysis; Iterative method; Mathematical optimization; Numerical stability; 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":[0.001159098,0.0005701606,0.0004793632,0.0006486832,0.0004371304,0.0009313711,0.0005095483,0.0005156005,0.002376218],"category_scores_gemma":[0.003786871,0.0002108342,0.0005427824,0.0005329042,0.0009562442,0.00112791,0.0009909265,0.001084513,0.0007273839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000319618,"about_ca_system_score_gemma":0.0005135581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005310774,"about_ca_topic_score_gemma":0.0006057044,"domain_scores_codex":[0.9994067,0.0001721558,0.00003841571,0.00005690581,0.0002789258,0.00004694535],"domain_scores_gemma":[0.998129,0.0008568325,0.0001694012,0.0004149631,0.00039234,0.00003750193],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003178244,0.0001742845,0.001925763,0.0004400091,0.00005179462,0.000480692,0.000834901,0.1883715,0.1200365,0.3304975,0.003878228,0.3529908],"study_design_scores_gemma":[0.00001441797,0.0000607377,0.0003010492,0.00002258256,0.00001357435,0.0001127786,0.00003574967,0.894951,0.05514583,0.04324102,0.00608085,0.0000204031],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02373829,0.0002081899,0.9711212,0.0002009895,0.0001044637,0.00002790062,0.0000169845,0.0005427966,0.004039139],"genre_scores_gemma":[0.3593867,0.0003979994,0.6363408,0.0001061689,0.00008373958,0.0000709097,0.00006315506,0.0002329986,0.003317494],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002376218,"threshold_uncertainty_score":0.007949293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.225338699917016,"score_gpt":0.4653882533224481,"score_spread":0.2400495534054321,"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."}}