{"id":"W2114005214","doi":"10.1142/s0218202501000702","title":"A METHOD FOR THE FORWARD MODELLING OF 3-D ELECTROMAGNETIC QUASI-STATIC PROBLEMS","year":2001,"lang":"en","type":"article","venue":"Mathematical Models and Methods in Applied Sciences","topic":"Advanced Numerical Methods in Computational Mathematics","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Discretization; Preconditioner; Mathematics; Applied mathematics; Biconjugate gradient stabilized method; Algebraic number; Linear system; Mathematical analysis","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":[0.0004516121,0.0005721429,0.0005415352,0.0005003516,0.0005804938,0.0005592097,0.0009939915,0.00127691,0.003569675],"category_scores_gemma":[0.0008512426,0.000323791,0.0007061713,0.0003919788,0.0006664174,0.0006895557,0.0008619768,0.001129488,0.001477171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002791891,"about_ca_system_score_gemma":0.0009937704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001588392,"about_ca_topic_score_gemma":0.002172936,"domain_scores_codex":[0.9998229,0.00005167105,0.000007571738,0.0000149569,0.00009300071,0.00000986084],"domain_scores_gemma":[0.9997657,0.0001028652,0.00002073878,0.00004018163,0.00005253433,0.00001801485],"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":[0.00006811577,0.00009601045,0.0004580005,0.0004707495,0.00007076221,0.000271466,0.0002235796,0.532198,0.04834558,0.276806,0.007578534,0.1334133],"study_design_scores_gemma":[0.00001480356,0.00002153745,0.00005693577,0.00001964762,0.000007366649,0.00009862992,0.00001257695,0.963145,0.00220031,0.01676956,0.01763817,0.00001537011],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0007686186,0.00005724804,0.9977614,0.00006075714,0.00004251308,0.00002498736,0.00003378982,0.0001571838,0.001093513],"genre_scores_gemma":[0.04433518,0.0002983529,0.9501963,0.00007722542,0.00003893736,0.0003224374,0.00009681575,0.0001365042,0.004498295],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003569675,"threshold_uncertainty_score":0.01194173,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1049150186294104,"score_gpt":0.3911786008588531,"score_spread":0.2862635822294427,"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."}}