{"id":"W4313591746","doi":"10.1109/jmmct.2022.3233944","title":"A Systematic Approach to Adaptive Mesh Refinement for Computational Electrodynamics","year":2023,"lang":"en","type":"article","venue":"IEEE journal on multiscale and multiphysics computational techniques","topic":"Computational Fluid Dynamics and Aerodynamics","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; National Aeronautics and Space Administration","keywords":"Adaptive mesh refinement; Computer science; Computational science","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004044955,0.0003224322,0.0004189837,0.000416154,0.0002851905,0.0001492743,0.0001924359,0.00008793834,5.522731e-7],"category_scores_gemma":[0.00003615813,0.0003106449,0.0001703306,0.0004078651,0.00003663916,0.0001461475,0.00003520284,0.0002859679,0.0000124878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002202071,"about_ca_system_score_gemma":0.00005031053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001882502,"about_ca_topic_score_gemma":0.000001730735,"domain_scores_codex":[0.9981625,0.00005009044,0.0006102693,0.0003139578,0.0005008769,0.0003623018],"domain_scores_gemma":[0.9985113,0.000669939,0.0001211638,0.0001138193,0.0003903811,0.0001934181],"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":[0.0000371801,0.0001014638,0.000007427366,0.0005609615,0.0001459656,0.00000354449,0.0001616648,0.96635,0.0006192214,0.02712298,0.001382405,0.003507209],"study_design_scores_gemma":[0.0004866886,0.0002342872,0.000554873,0.0004769365,0.0000339301,0.00004033811,0.00004834069,0.968277,0.00007429034,0.02937026,0.00006038273,0.0003426353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05078947,0.00003096124,0.946715,0.0001550971,0.0002502787,0.001158441,0.0001629809,0.0005354494,0.0002023432],"genre_scores_gemma":[0.712558,0.00005513059,0.2861346,0.0002331972,0.0002755719,0.0003305524,0.0002584608,0.00008255766,0.00007188998],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6617686,"threshold_uncertainty_score":0.9999346,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01411778013879554,"score_gpt":0.2534436607663798,"score_spread":0.2393258806275843,"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."}}