{"id":"W2119955016","doi":"10.1109/tbme.2009.2032163","title":"Adaptive Mesh Refinement Techniques for 3-D Skin Electrode Modeling","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Electrical and Bioimpedance Tomography","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Adaptive mesh refinement; Electrical impedance tomography; Finite element method; Computer science; Mesh generation; Electrical impedance; Electrode; Algorithm; Process (computing); Computational science; Electrical engineering; Engineering; 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":[0.0006323179,0.000473284,0.0004258858,0.0006465603,0.0002409639,0.0004174528,0.001001348,0.0008201271,0.001791303],"category_scores_gemma":[0.002404061,0.0004278783,0.0008001681,0.0005905491,0.0004316319,0.0007307913,0.0008153337,0.0009129873,0.0007308289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003693802,"about_ca_system_score_gemma":0.0004332941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002383499,"about_ca_topic_score_gemma":0.00219164,"domain_scores_codex":[0.9996045,0.0001337316,0.00002264229,0.00003363701,0.0001882802,0.00001716219],"domain_scores_gemma":[0.9993868,0.0003659785,0.00004190857,0.00007832434,0.0001122305,0.00001468186],"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":[0.00005110417,0.00002303722,0.0005883083,0.0001561385,0.00004261211,0.0001361951,0.0001950675,0.8111176,0.02461117,0.03479927,0.001600118,0.1266794],"study_design_scores_gemma":[0.00000640995,0.000008627135,0.00006020089,0.000009662357,0.000003989438,0.00004447045,0.000008530765,0.9880667,0.002085841,0.006008527,0.003690125,0.000006953066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001137263,0.00008269328,0.998252,0.00003281863,0.00001147132,0.000009016943,0.00001084837,0.00009528166,0.0003686525],"genre_scores_gemma":[0.1030462,0.0005215904,0.894259,0.00006553128,0.00002541274,0.0001389475,0.0001072876,0.0001439163,0.001692167],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002383499,"threshold_uncertainty_score":0.005992472,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008486064148958713,"score_gpt":0.2126456704858078,"score_spread":0.2041596063368491,"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."}}