{"id":"W2164681381","doi":"10.1109/radar.2011.5960596","title":"An adaptive hierarchal CFAR for optimal target detection in mixed clutter environments","year":2011,"lang":"en","type":"article","venue":"","topic":"Radar Systems and Signal Processing","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Raytheon Technologies (Canada)","funders":"","keywords":"Clutter; Constant false alarm rate; Computer science; Homogeneous; False alarm; Scheme (mathematics); Artificial intelligence; Pattern recognition (psychology); Radar; Mathematics; 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":[0.0006782086,0.0003734645,0.0003760501,0.0005450617,0.0002283679,0.0003439999,0.0005643003,0.0002808382,0.0007400108],"category_scores_gemma":[0.001064518,0.0001868848,0.0003705824,0.000457537,0.0002921266,0.0003528774,0.0003128117,0.0004640031,0.0003793451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002132311,"about_ca_system_score_gemma":0.000452204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008981267,"about_ca_topic_score_gemma":0.001509612,"domain_scores_codex":[0.9996079,0.00009910089,0.00001446172,0.00006851849,0.0001698649,0.00004034261],"domain_scores_gemma":[0.9996175,0.0001146129,0.00004845418,0.00007661138,0.0001193752,0.00002340025],"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":[0.000421912,0.0001182258,0.001258623,0.0001447152,0.00008907187,0.0001017004,0.000111505,0.116569,0.2131502,0.01639893,0.002834742,0.6488014],"study_design_scores_gemma":[0.0000364318,0.0003193473,0.001725725,0.0000103789,0.00004770618,0.0003495567,0.00002401138,0.9485248,0.0402579,0.0036385,0.005013902,0.00005181268],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01335158,0.0001272846,0.9852195,0.00003043429,0.00001708942,0.00001950244,0.00001975652,0.0004783385,0.0007364592],"genre_scores_gemma":[0.2326953,0.0001428121,0.76595,0.00006621048,0.00002966775,0.00003264536,0.00007972035,0.00005333293,0.0009503258],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0008981267,"threshold_uncertainty_score":0.003586769,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02039706852483384,"score_gpt":0.2002310640858938,"score_spread":0.17983399556106,"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."}}