{"id":"W2557100757","doi":"10.1190/geo2016-0004.1","title":"3D vector finite-element electromagnetic forward modeling for large loop sources using a total-field algorithm and unstructured tetrahedral grids","year":2016,"lang":"en","type":"article","venue":"Geophysics","topic":"Geophysical and Geoelectrical Methods","field":"Earth and Planetary Sciences","cited_by":98,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"China Scholarship Council; Memorial University of Newfoundland; National Natural Science Foundation of China","keywords":"Solver; Loop (graph theory); Tetrahedron; Finite element method; Algorithm; Computer science; Matrix (chemical analysis); Geometry; Mathematics; Physics; Materials science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0003765433,0.0005526583,0.0004174017,0.0003981502,0.0003376712,0.0007044909,0.001002842,0.0008857723,0.0034178],"category_scores_gemma":[0.0009133818,0.0003219892,0.0005890889,0.0003299649,0.0004519935,0.0004915166,0.0005066277,0.0005187418,0.0006036659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004808671,"about_ca_system_score_gemma":0.001005962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00750105,"about_ca_topic_score_gemma":0.006273819,"domain_scores_codex":[0.999854,0.0000334503,0.000008834199,0.00001519182,0.000074475,0.00001401562],"domain_scores_gemma":[0.9995596,0.0002030791,0.00004206819,0.00005470097,0.0001225485,0.00001805147],"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":[0.00001680916,0.00002631925,0.0004918459,0.00002833135,0.000008913052,0.0000489702,0.00005257821,0.9838684,0.004223584,0.00321986,0.0004710598,0.007543385],"study_design_scores_gemma":[0.000004070651,0.000006494517,0.00004190323,0.000002639918,9.472317e-7,0.000007934356,0.000005834793,0.9981462,0.0008344257,0.0003630922,0.0005836166,0.000002875355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05160565,0.00006240842,0.9404602,0.0001566039,0.00004226359,0.00009276949,0.0002010264,0.001452497,0.005926562],"genre_scores_gemma":[0.5669942,0.0001250806,0.4251954,0.00008008559,0.00001904334,0.0002668389,0.0004631664,0.0004278943,0.006428299],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00750105,"threshold_uncertainty_score":0.01491475,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01333245585625412,"score_gpt":0.2341511128979326,"score_spread":0.2208186570416785,"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."}}