{"id":"W2157359660","doi":"10.1109/aps.1994.408133","title":"An improved iterative method for inverse scattering","year":2002,"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":"Microwave imaging; Iterative method; Inverse scattering problem; Convergence (economics); Inverse problem; Iterative reconstruction; Permittivity; Inverse; Rate of convergence; Scattering; Born approximation; Cylinder; Stability (learning theory); Mathematical analysis; Applied mathematics; Mathematics; Computer science; Algorithm; Dielectric; Microwave; Optics; Physics; Geometry; Artificial intelligence","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":[0.0001225193,0.0001285456,0.0001486928,0.00009709064,0.00005923415,0.00008466833,0.0001107637,0.00003417529,0.0003603478],"category_scores_gemma":[0.000007824871,0.0001228831,0.00008080106,0.0001055476,0.0000126144,0.0001574592,0.000009241136,0.00006756664,0.00004623379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003000568,"about_ca_system_score_gemma":0.000001126789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002987385,"about_ca_topic_score_gemma":0.00002381566,"domain_scores_codex":[0.9994036,0.00001826673,0.0001444188,0.0001787958,0.00004028059,0.0002146436],"domain_scores_gemma":[0.9996152,0.00003292561,0.00001306675,0.0002361657,0.00002490836,0.00007772183],"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":[0.000002215099,0.00002190718,0.00009715921,0.00006037033,0.0001100432,0.00000141856,0.001322077,0.01662968,0.9401314,0.00003046366,0.01333599,0.02825725],"study_design_scores_gemma":[0.0001689752,0.00001987482,0.00001835395,0.000006233553,0.00002355362,0.000004390857,0.00008220656,0.950502,0.04525485,0.00004177795,0.003710542,0.0001672893],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03692662,0.00005358128,0.9586856,0.0002096408,0.00009533714,0.0001012759,0.00001060563,0.0005368578,0.003380508],"genre_scores_gemma":[0.7471823,0.000009035566,0.2505948,0.0003185645,0.00009829951,0.00003379487,0.000009115858,0.00003768972,0.001716479],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9338723,"threshold_uncertainty_score":0.5011027,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01725882395890687,"score_gpt":0.2582042010255667,"score_spread":0.2409453770666599,"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."}}