{"id":"W2740721376","doi":"10.1109/iwagpr.2017.7996101","title":"Spatially-filtered FDTD subgridding for ground penetrating radar numerical modeling","year":2017,"lang":"en","type":"article","venue":"","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Finite-difference time-domain method; Ground-penetrating radar; Limit (mathematics); Radar; Computer science; Stability (learning theory); Scheme (mathematics); Relative permittivity; Permittivity; Mathematics; Engineering; Optics; Mathematical analysis; Physics; Telecommunications; Machine learning; Dielectric","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":[0.0001946497,0.0002872446,0.0002158471,0.0002211137,0.0001835669,0.0003143474,0.0003997215,0.0004925482,0.001236158],"category_scores_gemma":[0.000622806,0.0001515171,0.0003029174,0.0002761363,0.0002269494,0.0004107965,0.0002715536,0.0004694634,0.0004151131],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003155437,"about_ca_system_score_gemma":0.0004565102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002259804,"about_ca_topic_score_gemma":0.001826745,"domain_scores_codex":[0.9999405,0.00001576275,0.00000442539,0.000007107696,0.00002633018,0.000005844133],"domain_scores_gemma":[0.9998499,0.00005653252,0.00001286553,0.00004067379,0.00003370287,0.000006231504],"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":[0.00006908263,0.00006314434,0.001373721,0.0001376737,0.00002572101,0.0001876063,0.0002215511,0.7193858,0.08408971,0.06709045,0.002333193,0.1250224],"study_design_scores_gemma":[0.000002327493,0.000006390668,0.00006287527,0.000003325917,0.000001727838,0.00001936444,0.000004767642,0.993333,0.002902656,0.001611587,0.002049395,0.000002653912],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01000316,0.0001022608,0.9882237,0.0000438121,0.0000270275,0.00001582809,0.00003170981,0.000252003,0.001300622],"genre_scores_gemma":[0.2498394,0.0004456738,0.7461309,0.00005340642,0.00001805027,0.0001050287,0.0001531679,0.0001004594,0.003153787],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002259804,"threshold_uncertainty_score":0.004493296,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05379259876547984,"score_gpt":0.3066310653826642,"score_spread":0.2528384666171843,"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."}}