{"id":"W2077936074","doi":"10.1155/2008/593216","title":"Adaptive S-Method for SAR/ISAR Imaging","year":2007,"lang":"en","type":"article","venue":"EURASIP Journal on Advances in Signal Processing","topic":"Advanced SAR Imaging Techniques","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Inverse synthetic aperture radar; Computer science; Artificial intelligence; Computer vision; Radar imaging; Synthetic aperture radar; Fourier transform; Radar; Telecommunications; Mathematics","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":[0.0002365635,0.0004157432,0.0003086863,0.0004423764,0.0001827872,0.0002365337,0.0006134903,0.0005645143,0.002360844],"category_scores_gemma":[0.0003727986,0.0001459477,0.0004259747,0.0004164254,0.0003963294,0.0003472082,0.0004730244,0.0005080245,0.001247867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001554019,"about_ca_system_score_gemma":0.0003234544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006112701,"about_ca_topic_score_gemma":0.0008130977,"domain_scores_codex":[0.9998125,0.00005196682,0.000006352743,0.00002512707,0.00009440987,0.000009695935],"domain_scores_gemma":[0.9998748,0.00003731387,0.00001543055,0.00001698213,0.00004453475,0.00001089526],"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":[0.0001779391,0.00006525414,0.0008234996,0.0003132547,0.00007680638,0.0002463858,0.0001048701,0.09833563,0.1925096,0.07282652,0.009403486,0.6251168],"study_design_scores_gemma":[0.00002654207,0.00009518477,0.0004348865,0.00001557962,0.0000135235,0.0003489489,0.00001364881,0.938674,0.02000676,0.01402011,0.02632113,0.00002968126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001855721,0.0002621984,0.9964845,0.00005685203,0.00004424344,0.00001279035,0.00001322132,0.0001703452,0.001100035],"genre_scores_gemma":[0.07333326,0.000641344,0.9200547,0.0001214803,0.0001322964,0.00007598268,0.0001028149,0.0001130204,0.005425184],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002360844,"threshold_uncertainty_score":0.007897854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01512649353287615,"score_gpt":0.3395792242860114,"score_spread":0.3244527307531352,"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."}}