{"id":"W3198681040","doi":"10.1364/optica.446511","title":"Inverse problem solver for multiple light scattering using modified Born series","year":2022,"lang":"en","type":"article","venue":"Optica","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea","keywords":"Inverse scattering problem; Inverse problem; Solver; Scattering; Born approximation; Series (stratigraphy); Inversion (geology); Inverse; Diffraction tomography; Diffraction; Computer science; Quantum inverse scattering method; Algorithm; Optics; Physics; Mathematical optimization; Mathematics; Mathematical analysis; Inverse scattering transform; Geometry","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":[0.000159949,0.0001554863,0.0001931852,0.000119576,0.0003085961,0.00007352148,0.0001653624,0.00002666606,0.0001048199],"category_scores_gemma":[0.00001176934,0.0001776455,0.0001101132,0.0001736583,0.00002474151,0.0001259577,0.0001037201,0.0001439865,0.00001058496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009928515,"about_ca_system_score_gemma":0.00001507962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003840259,"about_ca_topic_score_gemma":0.00001240181,"domain_scores_codex":[0.9991158,0.00001682534,0.0001993976,0.0002142083,0.0001246827,0.0003290624],"domain_scores_gemma":[0.9996014,0.00002567488,0.00002814459,0.000255681,0.00002411732,0.00006495386],"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":[0.00001680499,0.00002532285,0.0002591142,0.0001188327,0.0001266891,0.000006769633,0.0006350811,0.5386812,0.4557734,0.00005312204,0.003871665,0.000431969],"study_design_scores_gemma":[0.0003748168,0.0000274168,0.00001872401,0.00002466267,0.00009436223,0.00002750446,0.0003327199,0.9509199,0.02707358,0.00009268755,0.02067625,0.0003374012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9285887,0.000214652,0.06297681,0.0009965185,0.0005442582,0.000495315,0.0001054793,0.0008445622,0.005233685],"genre_scores_gemma":[0.9531311,0.000006270425,0.04588385,0.00007477884,0.00007404036,0.0001069386,0.00002348718,0.00006173449,0.0006377824],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4286999,"threshold_uncertainty_score":0.7244175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01752713184863834,"score_gpt":0.2130064314382572,"score_spread":0.1954792995896188,"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."}}