{"id":"W2125302771","doi":"10.1109/nrc.1991.114742","title":"A robust automatic censored CFAR detector for nonhomogeneous environments","year":2002,"lang":"en","type":"article","venue":"","topic":"Radar Systems and Signal Processing","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"New York Institute of Technology","funders":"","keywords":"Clutter; Constant false alarm rate; Detector; Censoring (clinical trials); Computer science; False alarm; Noise (video); Algorithm; Artificial intelligence; Statistics; Mathematics; Pattern recognition (psychology); 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004340279,0.0001196727,0.0001402396,0.00003574457,0.00006334112,0.00003607342,0.00008255681,0.00005611669,0.0004561916],"category_scores_gemma":[0.000008554163,0.0001064723,0.00005346261,0.00004644092,0.000009738311,0.00006715367,0.000006697408,0.00004020441,0.0001440083],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004747074,"about_ca_system_score_gemma":0.000001362195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003653416,"about_ca_topic_score_gemma":0.000003822592,"domain_scores_codex":[0.9993671,0.000005929192,0.0001841504,0.0001194111,0.0001020892,0.0002212992],"domain_scores_gemma":[0.9997596,0.00002705604,0.00001847835,0.0001279502,0.000004296166,0.0000625981],"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":[0.00001132113,0.0001871744,0.0002572619,0.001928618,0.0004112201,0.00007522818,0.001894697,0.3454561,0.2588378,0.0001824048,0.03528208,0.3554761],"study_design_scores_gemma":[0.0002731824,0.00002564407,0.00005338714,0.00002273853,0.00001107901,0.00001816233,0.00002732232,0.9788024,0.008381371,0.00001544149,0.01220374,0.0001655227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3554267,0.002631109,0.6222751,0.00005198587,0.0006642186,0.0008825111,0.00002778528,0.001091471,0.01694913],"genre_scores_gemma":[0.9851899,0.00001113755,0.01228683,0.00002477924,0.00008763954,0.0000354273,0.000001673513,0.00004137079,0.002321266],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6333463,"threshold_uncertainty_score":0.4994978,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02155845282294218,"score_gpt":0.1782591419715006,"score_spread":0.1567006891485584,"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."}}