{"id":"W3007279731","doi":"10.5515/kjkiees.2018.29.7.523","title":"Analysis Technique for Moving Targets on Single-Channel Airborne FMCW-SAR Image","year":2018,"lang":"en","type":"article","venue":"The Journal of Korean Institute of Electromagnetic Engineering and Science","topic":"Advanced SAR Imaging Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"National Research Foundation of Korea","keywords":"Synthetic aperture radar; Ambiguity function; Remote sensing; Computer science; SIGNAL (programming language); Channel (broadcasting); Computer vision; Geology; Artificial intelligence; Radar; 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":[0.0002408727,0.0004577937,0.0003165003,0.0006984733,0.0002128177,0.0003282192,0.0003448086,0.00035597,0.001280892],"category_scores_gemma":[0.0004667969,0.0001874883,0.0004136969,0.0004597579,0.0002314416,0.0007564985,0.0002682996,0.0003720009,0.0003408926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001766831,"about_ca_system_score_gemma":0.0003254697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005607653,"about_ca_topic_score_gemma":0.0005426373,"domain_scores_codex":[0.9997928,0.00001624022,0.00000659748,0.00004211928,0.0001248975,0.00001749533],"domain_scores_gemma":[0.999799,0.00006110003,0.00002710031,0.00002067594,0.00008373053,0.00000839434],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001259468,0.00004082048,0.0013641,0.0002502726,0.00006091794,0.0002569204,0.0002426032,0.0419482,0.5717748,0.007257639,0.001113477,0.3755643],"study_design_scores_gemma":[0.00001863232,0.0001940191,0.003260179,0.00002323168,0.00008405252,0.0009035034,0.0001212976,0.809001,0.1772606,0.003150951,0.005938948,0.00004354678],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02160978,0.0001226335,0.9772357,0.00002911356,0.00002136673,0.00001306072,0.00001496358,0.00028857,0.0006648418],"genre_scores_gemma":[0.376191,0.0005292682,0.6201334,0.00006036238,0.00006714697,0.00007444098,0.0001287179,0.0001114103,0.002704222],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001280892,"threshold_uncertainty_score":0.004285038,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007732426875556597,"score_gpt":0.2275548650150381,"score_spread":0.2198224381394815,"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."}}