{"id":"W2904141483","doi":"10.1109/antem.2018.8573043","title":"Regularization Approaches for a Non-Iterative Eigenfunction-Based Electromagnetic Inversion Algorithm","year":2018,"lang":"en","type":"article","venue":"","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Regularization (linguistics); Eigenfunction; Inverse problem; Iterative method; Algorithm; Inversion (geology); Electromagnetics; Solver; Computer science; Mathematical optimization; Mathematics; Applied mathematics; Artificial intelligence; Mathematical analysis; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008759094,0.0001167989,0.0001098172,0.0001314727,0.0001086537,0.00005516401,0.00006375781,0.000047585,0.00008531525],"category_scores_gemma":[0.000008644396,0.0001109832,0.00006626643,0.0002280686,0.00003654766,0.00006672269,0.000005912941,0.00004727981,0.0000354259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005173229,"about_ca_system_score_gemma":0.00001341438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001027506,"about_ca_topic_score_gemma":0.00000607791,"domain_scores_codex":[0.9994383,0.00001214973,0.0001236067,0.0001744975,0.00007191765,0.0001795077],"domain_scores_gemma":[0.9997026,0.00001976418,0.00001722737,0.0001576246,0.0000619547,0.0000408565],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006633905,0.0001446733,0.0004234908,0.0003398709,0.0005080926,0.000002223661,0.001986156,0.03521757,0.549709,0.000397026,0.0658287,0.3453769],"study_design_scores_gemma":[0.0002295663,0.0001029963,0.00005430474,0.00001002033,0.00003428363,7.7221e-7,0.00002938493,0.8837451,0.1146202,0.0001332056,0.0009143286,0.0001258377],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01995107,0.00002206976,0.9771156,0.0001014301,0.0001182187,0.0001349791,0.000004512875,0.0002230634,0.002329034],"genre_scores_gemma":[0.8887446,0.000002126719,0.1088574,0.0001347131,0.0002153857,0.00004444316,0.0001135163,0.00003168067,0.001856188],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8687935,"threshold_uncertainty_score":0.4525765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01189791206374062,"score_gpt":0.1952159455165972,"score_spread":0.1833180334528566,"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."}}