{"id":"W2009936010","doi":"10.1117/12.819361","title":"CFAR detection and extraction of maneuvering air target in strong sea-clutter via time-frequency-based S-method","year":2009,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Radar Systems and Signal Processing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Clutter; Computer science; Constant false alarm rate; Time–frequency analysis; Extraction (chemistry); Remote sensing; Artificial intelligence; Radar; Computer vision; Filter (signal processing); Telecommunications; 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":[0.0005569171,0.0004076135,0.0003892046,0.0008808085,0.0001366482,0.0003596953,0.0003486581,0.000618904,0.000924232],"category_scores_gemma":[0.001422038,0.0001421921,0.0003369813,0.0004485631,0.0003141233,0.0005766631,0.0003253828,0.0002416278,0.0005042722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001309219,"about_ca_system_score_gemma":0.0002513363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000426629,"about_ca_topic_score_gemma":0.0005750462,"domain_scores_codex":[0.9996904,0.00008479917,0.00001735383,0.00004542666,0.0001390682,0.00002288164],"domain_scores_gemma":[0.9994455,0.0002464742,0.00007672505,0.00006430186,0.0001487069,0.00001833054],"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":[0.000504688,0.00009982979,0.002446012,0.0001747029,0.0000629058,0.00019028,0.0001063267,0.04024626,0.415401,0.007101093,0.001007228,0.5326598],"study_design_scores_gemma":[0.00004010033,0.000210515,0.002159754,0.00001524336,0.00002785869,0.0006676181,0.00002308063,0.8923866,0.1000839,0.002049479,0.00229829,0.00003757114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05918569,0.000254184,0.9391171,0.00007193609,0.00003338477,0.00001589816,0.00003073965,0.0004280054,0.0008630421],"genre_scores_gemma":[0.3199012,0.0002503843,0.6781946,0.00008301382,0.00004435339,0.00002770439,0.00008145416,0.00003453446,0.001382579],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.000924232,"threshold_uncertainty_score":0.003091872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00691718495783337,"score_gpt":0.2235176159618668,"score_spread":0.2166004310040334,"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."}}