{"id":"W2095780745","doi":"10.1109/tgrs.2009.2037010","title":"Experimental Verification of SAR-GMTI Improvement Through Antenna Switching","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Advanced SAR Imaging Techniques","field":"Engineering","cited_by":69,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"Institut National des Sciences Appliquées de Lyon","keywords":"Moving target indication; Computer science; Synthetic aperture radar; Constant false alarm rate; Inverse synthetic aperture radar; Antenna (radio); Radar imaging; Radar; Terrain; False alarm; Remote sensing; Artificial intelligence; Computer vision; Real-time computing; Continuous-wave radar; Telecommunications; Geology; Geography","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":[0.00009300003,0.0001149561,0.0001065245,0.00008068247,0.0001597726,0.00002480935,0.00007013583,0.00004826199,0.000002621375],"category_scores_gemma":[0.000002919623,0.0001116845,0.00003407936,0.0001601821,0.0001147983,0.0002770148,0.000001302295,0.0002385212,0.000001497707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002754469,"about_ca_system_score_gemma":0.0000100072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001334453,"about_ca_topic_score_gemma":0.00001455415,"domain_scores_codex":[0.9993264,0.000006257703,0.000167481,0.0001926247,0.0001354163,0.000171838],"domain_scores_gemma":[0.9996656,0.00001977125,0.00003369018,0.0002157719,0.00002769806,0.00003744365],"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":[0.000002583257,0.000008151755,1.384373e-7,0.000007446731,0.000002266491,7.698817e-7,0.0003509724,0.000145082,0.7274572,0.000005677335,9.989101e-7,0.2720187],"study_design_scores_gemma":[0.00007786225,0.00003509283,0.00001970384,0.00003828311,0.000004666417,0.00002012818,0.0001740098,0.2350244,0.7641984,0.0002194044,0.00009047546,0.00009764088],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3465059,0.00001872388,0.6526895,0.0000299076,0.0003978575,0.00007898954,0.000001692824,0.0001641919,0.0001133313],"genre_scores_gemma":[0.7490211,0.00003831828,0.2508715,0.00002879275,0.00001444905,1.006918e-7,2.26169e-7,0.00001243979,0.00001316121],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4025152,"threshold_uncertainty_score":0.455436,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01124477874761827,"score_gpt":0.256238778176482,"score_spread":0.2449939994288637,"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."}}