{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001075166,0.000631597,0.0006252746,0.000483996,0.0003169106,0.0007669536,0.0009556714,0.001268671,0.00429114],"category_scores_gemma":[0.002569858,0.0002972154,0.0008627792,0.0004206903,0.0006480223,0.0009738221,0.001379359,0.001910265,0.001156113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000437893,"about_ca_system_score_gemma":0.001053706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009733295,"about_ca_topic_score_gemma":0.001062672,"domain_scores_codex":[0.9995925,0.0001131864,0.00002267846,0.00003872088,0.0002113384,0.00002153161],"domain_scores_gemma":[0.9991606,0.0005075377,0.0000555114,0.00005687352,0.0001910622,0.00002835267],"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":[0.0000717516,0.00007483947,0.0005512144,0.0004069619,0.0000584536,0.0004399554,0.0003152843,0.515569,0.02310379,0.3562392,0.006368251,0.09680126],"study_design_scores_gemma":[0.000009251768,0.00001119518,0.00002623459,0.000009874742,0.000003725973,0.00007564369,0.0000160677,0.9791486,0.001871725,0.01526547,0.003556072,0.000006172641],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001706898,0.00007003368,0.9953756,0.0001009198,0.00003843814,0.00002253718,0.0000283487,0.00008842134,0.002568692],"genre_scores_gemma":[0.09445825,0.0003958542,0.8930591,0.0001904807,0.00009323614,0.0002907965,0.000169231,0.0003191427,0.01102382],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00429114,"threshold_uncertainty_score":0.0143553,"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."}}