{"id":"W2388633183","doi":"","title":"A New CFAR Detector Based on Fuzzy Logic","year":2008,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Radar Systems and Signal Processing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Constant false alarm rate; Computer science; Detector; Fuzzy logic; Lagging; False alarm; Statistic; Variance (accounting); Algorithm; Pattern recognition (psychology); Data mining; Artificial intelligence; Statistics; Mathematics; Telecommunications","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00002805517,0.0001254707,0.0001169913,0.00007539625,0.0001361891,0.00002983292,0.0001675705,0.00005416767,0.00002391798],"category_scores_gemma":[1.612365e-7,0.0001211299,0.00005262644,0.0001993091,0.00001302609,0.00004115643,0.00001095676,0.0001022162,0.0002929051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000426328,"about_ca_system_score_gemma":0.00003131448,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001103065,"about_ca_topic_score_gemma":0.000001674403,"domain_scores_codex":[0.9994091,0.000006664711,0.0001608274,0.0001749354,0.00008496073,0.0001635598],"domain_scores_gemma":[0.9996486,0.00002445384,0.00002061551,0.0001986715,0.00002040146,0.00008725201],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001162085,0.0001490026,0.0003388929,0.0002763462,0.00006657879,0.00002562699,0.0006348834,0.2058495,0.1274581,0.001876673,0.09046157,0.5728512],"study_design_scores_gemma":[0.0005332289,0.00003945806,0.001088358,0.00004517007,0.00000989099,0.00006085647,0.000005982235,0.1002832,0.01434503,0.0006294184,0.8825415,0.0004179136],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003169321,0.0002604561,0.9906499,0.00007990137,0.00002456427,0.0002802169,0.000004103009,0.0004200052,0.005111497],"genre_scores_gemma":[0.7422711,0.000006309462,0.2563805,0.0003979745,0.0004723444,0.0001338583,0.00001101562,0.00004146963,0.0002853962],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7920799,"threshold_uncertainty_score":0.4939536,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01237232972567333,"score_gpt":0.2016624346517192,"score_spread":0.1892901049260459,"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."}}