{"id":"W2016012726","doi":"10.1007/s00211-009-0285-8","title":"A posteriori error estimation for hp-version time-stepping methods for parabolic partial differential equations","year":2010,"lang":"en","type":"article","venue":"Numerische Mathematik","topic":"Advanced Numerical Methods in Computational Mathematics","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Estimator; Discontinuous Galerkin method; Discretization; Partial differential equation; Time stepping; Galerkin method; A priori and a posteriori; Applied mathematics; Numerical analysis; Error analysis; Parabolic partial differential equation; Series (stratigraphy); Mathematical analysis; Finite element method; Statistics","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":[0.004448825,0.00107627,0.001228821,0.0007545128,0.0004993963,0.001641702,0.001370733,0.002112325,0.001887768],"category_scores_gemma":[0.01413116,0.0008133057,0.0008072086,0.000454656,0.001302232,0.001946451,0.002274862,0.002586837,0.0003312133],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005394534,"about_ca_system_score_gemma":0.001078238,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00290233,"about_ca_topic_score_gemma":0.001544852,"domain_scores_codex":[0.9988907,0.0006167919,0.00007298622,0.0001049605,0.0002690495,0.0000456252],"domain_scores_gemma":[0.9927763,0.005283991,0.0003501985,0.0004984126,0.0009335047,0.0001576236],"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":[0.0008424215,0.0002362978,0.001949849,0.0007495176,0.0002592569,0.000179595,0.0002880568,0.7611949,0.02285222,0.06279612,0.00243186,0.1462198],"study_design_scores_gemma":[0.000006864129,0.00001901475,0.00009687634,0.000008151324,0.000005787369,0.000009030552,0.000004007577,0.99524,0.001236833,0.003145409,0.0002212372,0.000006769314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01676667,0.0002977168,0.9817369,0.0001633997,0.0000837142,0.00003142197,0.00003586976,0.0001250222,0.0007593168],"genre_scores_gemma":[0.4118021,0.0007883411,0.5777667,0.0001627884,0.0002045808,0.0003010975,0.0003303503,0.0004261305,0.008218009],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004448825,"threshold_uncertainty_score":0.02352792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03550431527130006,"score_gpt":0.3816430805358426,"score_spread":0.3461387652645426,"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."}}