{"id":"W3117038344","doi":"10.1109/ccece53047.2021.9569112","title":"Millimeter-Wave Circular Synthetic Aperture Radar Imaging","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced SAR Imaging Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Synthetic aperture radar; Radar imaging; Inverse synthetic aperture radar; Extremely high frequency; Continuous-wave radar; Side looking airborne radar; Radar; Computer science; Microwave; Remote sensing; Channel (broadcasting); Electronic engineering; Acoustics; Optics; Geology; Physics; Telecommunications; Engineering; Computer vision","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":[0.0002530381,0.0003203902,0.0003331806,0.0002807945,0.000119585,0.0003921417,0.0003180517,0.0004726426,0.001227116],"category_scores_gemma":[0.00050277,0.0001157399,0.0001861635,0.0003479576,0.0003051615,0.0005618889,0.0003409531,0.000339559,0.0007695148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001401611,"about_ca_system_score_gemma":0.0001866305,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001363892,"about_ca_topic_score_gemma":0.0002870125,"domain_scores_codex":[0.999787,0.00003808147,0.000009777408,0.00004415128,0.000100115,0.00002085055],"domain_scores_gemma":[0.9997389,0.0000885162,0.00004352192,0.00005470254,0.00006133016,0.00001301579],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003140144,0.0001021577,0.001643161,0.0005298142,0.00005217726,0.0002344143,0.0001603095,0.01468884,0.6311126,0.02347823,0.002564913,0.3251192],"study_design_scores_gemma":[0.00006238335,0.0006756473,0.007102209,0.00006003308,0.00006243787,0.003008684,0.0001079413,0.3356505,0.5899639,0.006823692,0.05640786,0.00007480542],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04899132,0.001125194,0.9434249,0.0002816839,0.0001616688,0.000055072,0.0001064137,0.0005457539,0.005308014],"genre_scores_gemma":[0.3338143,0.001133952,0.6594762,0.0002856114,0.0002170065,0.00005318511,0.0003095752,0.00007024546,0.004639852],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001227116,"threshold_uncertainty_score":0.004105091,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01303062706804068,"score_gpt":0.2246400590246681,"score_spread":0.2116094319566274,"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."}}