{"id":"W3201973096","doi":"10.1109/taes.2021.3117895","title":"Multirotor UAV-Borne Repeat-Pass CSM-VideoSAR","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Aerospace and Electronic Systems","topic":"Advanced SAR Imaging Techniques","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Aeronautical Science Foundation of China; National Natural Science Foundation of China","keywords":"Multirotor; Synthetic aperture radar; Computer science; Interpolation (computer graphics); Radar imaging; Radar; Computer vision; Remote sensing; Artificial intelligence; Real-time computing; Engineering; Aerospace engineering; Telecommunications; Image (mathematics); Geology","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":[0.0001303654,0.0003623964,0.0002200297,0.0001283508,0.0001531382,0.0002423402,0.0003248905,0.0002966087,0.001277401],"category_scores_gemma":[0.0001789239,0.00007934668,0.0001418534,0.0002131032,0.0002179214,0.0003286043,0.0003121649,0.0003075671,0.0003393919],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001806796,"about_ca_system_score_gemma":0.0002616058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001508837,"about_ca_topic_score_gemma":0.002051842,"domain_scores_codex":[0.9998603,0.0000161826,0.000003578612,0.00004135617,0.00005541297,0.00002310258],"domain_scores_gemma":[0.9999026,0.00001192147,0.00001796149,0.00002716313,0.00002943194,0.00001084284],"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":[0.0006756076,0.0001791711,0.007029835,0.0002314225,0.00006923039,0.0003858383,0.000380615,0.05419839,0.7771664,0.007981391,0.00618809,0.145514],"study_design_scores_gemma":[0.0001226053,0.001387625,0.02313028,0.00002567006,0.00004225348,0.0009064865,0.0003586381,0.5419111,0.4031404,0.002119173,0.02675071,0.0001050734],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7718182,0.0003656172,0.209582,0.0002983389,0.0001548254,0.0001385315,0.0009875856,0.001244523,0.01541031],"genre_scores_gemma":[0.8921475,0.0001400512,0.1015207,0.0001102752,0.00003570334,0.00005954465,0.000950055,0.00005861138,0.004977446],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001508837,"threshold_uncertainty_score":0.004273295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006450929406864608,"score_gpt":0.2175914382902986,"score_spread":0.211140508883434,"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."}}