{"id":"W4399654550","doi":"10.1016/bs.aams.2024.03.003","title":"An anisotropic mesh adaptation method based on gradient recovery and optimal shape elements","year":2024,"lang":"en","type":"book-chapter","venue":"Advances in applied mechanics","topic":"Advanced Numerical Methods in Computational Mathematics","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Anisotropy; Adaptation (eye); Mathematics; Applied mathematics; Computer science; Mathematical optimization; Mathematical analysis; Physics; Optics","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":[0.0003083739,0.0005106021,0.0005346671,0.0003113417,0.00004291138,0.00003910311,0.0002118497,0.0002497516,0.00005659276],"category_scores_gemma":[0.00002978443,0.0005501916,0.00006963294,0.0001099581,0.0000219142,0.0001370845,0.00005231685,0.000595574,0.00002920606],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003018085,"about_ca_system_score_gemma":0.00002233674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":1.534765e-7,"about_ca_topic_score_gemma":0.000002293543,"domain_scores_codex":[0.9980616,0.00002075042,0.0006039822,0.0006144263,0.000409087,0.0002901391],"domain_scores_gemma":[0.9987676,0.0005891941,0.0001528945,0.0003513919,0.00003116397,0.0001077423],"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":[0.00002116363,0.0000115737,1.948212e-8,0.0002044476,0.00001530588,0.000006755136,0.00002702248,0.4145785,0.00004179673,0.4039281,0.000002886564,0.1811624],"study_design_scores_gemma":[0.0001533556,0.0001325083,6.061712e-8,0.000170398,0.00003152686,0.000001816156,0.00002156321,0.5284562,0.00007890422,0.4623348,0.008344244,0.0002747101],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000008482328,0.001165477,0.9528893,0.000008528474,0.0006919316,0.0005520498,0.00006051806,0.0002983104,0.0443254],"genre_scores_gemma":[0.00130085,0.001704579,0.9957269,0.0001181993,0.0001170099,0.0001261107,0.00007943573,0.0002134882,0.0006134703],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1808877,"threshold_uncertainty_score":0.9996949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01916561974417436,"score_gpt":0.2972055779404021,"score_spread":0.2780399581962277,"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."}}