{"id":"W2964376230","doi":"10.1108/hff-09-2018-0489","title":"Convergence and error analysis of an automatically differentiated finite volume based heat conduction code","year":2019,"lang":"en","type":"article","venue":"International Journal of Numerical Methods for Heat &amp Fluid Flow","topic":"Heat Transfer and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Discretization; Finite volume method; Finite element method; Mathematics; Applied mathematics; Thermal conduction; Convergence (economics); Heat equation; Automatic differentiation; Field (mathematics); Mathematical analysis; Algorithm; Physics; Mechanics; Thermodynamics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006154283,0.0001680927,0.0005544319,0.0004619012,0.00002565495,0.00004079906,0.0002216956,0.0001085874,0.0004555908],"category_scores_gemma":[0.0002303333,0.0001509546,0.0002645832,0.0003698638,0.00004318415,0.0002998574,0.00001153575,0.00015873,0.000002981306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008095715,"about_ca_system_score_gemma":0.00003892995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001320889,"about_ca_topic_score_gemma":0.000003122646,"domain_scores_codex":[0.9983609,0.0002018689,0.0007721664,0.0001667053,0.0003309237,0.0001674264],"domain_scores_gemma":[0.9985824,0.0005125356,0.00004610469,0.0001442617,0.0005482904,0.0001663943],"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":[0.0002336065,0.0001619417,0.002276898,0.00007060536,0.001371595,0.000001720359,0.0002269287,0.8969153,0.08617106,0.0000542802,0.00003785043,0.01247819],"study_design_scores_gemma":[0.001017675,0.0002067717,0.007316604,0.00004688226,0.0004204649,0.00001290037,0.00002009057,0.9841591,0.005992967,0.0001127615,0.0005478568,0.0001459474],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2511257,0.00007854439,0.7475383,0.0001587769,0.0008519886,0.0001200933,0.00007640041,0.00003367328,0.00001645459],"genre_scores_gemma":[0.5957195,0.00003589258,0.4040162,0.00005845154,0.00005433863,0.000004979644,0.00007739428,0.00002043938,0.00001288401],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.3445937,"threshold_uncertainty_score":0.6155751,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02759276974242163,"score_gpt":0.3428547491499218,"score_spread":0.3152619794075001,"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."}}