{"id":"W3008732642","doi":"10.5515/kjkiees.2017.28.9.723","title":"Simplified Factorizing-Technique for Airborne FMCW-SAR Image Reconstruction","year":2017,"lang":"en","type":"article","venue":"The Journal of Korean Institute of Electromagnetic Engineering and Science","topic":"Advanced SAR Imaging Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nexen (Canada); University of Manitoba","funders":"National Research Foundation of Korea","keywords":"Synthetic aperture radar; Computer science; Beamwidth; Computer vision; Inverse synthetic aperture radar; Image (mathematics); Artificial intelligence; Projection (relational algebra); Computational complexity theory; Back projection; Remote sensing; Radar imaging; Algorithm; Radar; Geology; Telecommunications","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":[0.0003106129,0.000771685,0.0004701577,0.0003877891,0.0002089837,0.0004149355,0.0004934944,0.0004872183,0.002625806],"category_scores_gemma":[0.0007273906,0.0002589883,0.0006893681,0.0006397777,0.000370718,0.0007481584,0.0004610773,0.0007427655,0.0009809625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002030247,"about_ca_system_score_gemma":0.000472004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008254337,"about_ca_topic_score_gemma":0.0008877013,"domain_scores_codex":[0.999688,0.00005263144,0.00001557083,0.00005102075,0.0001692443,0.00002350681],"domain_scores_gemma":[0.9997725,0.00006040734,0.00002679307,0.00005778788,0.00007562109,0.00000686892],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002391865,0.00006312543,0.0007939332,0.000536069,0.00008761532,0.0003051721,0.0002395447,0.07694281,0.3969225,0.02674424,0.004085444,0.4930403],"study_design_scores_gemma":[0.00006500899,0.0002755399,0.00168918,0.00004904638,0.00008964373,0.00152405,0.00007264002,0.7789572,0.1695986,0.0110165,0.03657414,0.00008837083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00286074,0.0001152793,0.9962374,0.00002728219,0.00002066303,0.00002087627,0.00002216196,0.0001821372,0.0005133259],"genre_scores_gemma":[0.07048955,0.0005987733,0.9261968,0.00005667258,0.00006084767,0.0001021032,0.0002097023,0.00009160454,0.002193944],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002625806,"threshold_uncertainty_score":0.008784175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01022646784557081,"score_gpt":0.2468669240099767,"score_spread":0.2366404561644059,"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."}}