{"id":"W2145341514","doi":"10.1109/cdc.1997.657092","title":"Adaptive CFAR active sonar signal thresholding using radial basis functional neural networks","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":"Royal Military College of Canada","funders":"","keywords":"Thresholding; Sonar; Constant false alarm rate; Marine mammals and sonar; False alarm; Computer science; Noise (video); SIGNAL (programming language); Signal-to-noise ratio (imaging); Underwater; Artificial intelligence; Energy (signal processing); Artificial neural network; Range (aeronautics); Sonar signal processing; Computer vision; Signal processing; Mathematics; Telecommunications; Radar; Engineering; Statistics","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":[0.0005804437,0.0004354,0.0004703525,0.0002691574,0.0001823504,0.0003844192,0.0009107309,0.0007016446,0.0005940563],"category_scores_gemma":[0.001511021,0.0002313235,0.000312685,0.0003005926,0.0003311899,0.0005546155,0.0002996366,0.0005558663,0.0003525284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003042928,"about_ca_system_score_gemma":0.0003028342,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001856089,"about_ca_topic_score_gemma":0.002251056,"domain_scores_codex":[0.9997165,0.00007569766,0.00001479613,0.00005690337,0.0001076646,0.00002847919],"domain_scores_gemma":[0.9996052,0.0001444174,0.00004367797,0.00004030478,0.0001546865,0.00001162088],"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":[0.0002106315,0.0001016657,0.0008462221,0.0001212377,0.00006053362,0.000123141,0.00009073059,0.2354567,0.1048003,0.01309841,0.00120277,0.6438876],"study_design_scores_gemma":[0.000008247963,0.00004470938,0.0002654688,0.000004728941,0.00001078482,0.00005323546,0.000002542252,0.9889241,0.009415289,0.0007194084,0.0005396965,0.00001179667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01074735,0.0001380973,0.9880692,0.00003573188,0.00001932593,0.00001760804,0.000007956741,0.000379781,0.0005848058],"genre_scores_gemma":[0.334221,0.0002358578,0.6634188,0.00007232533,0.00003803986,0.00008451785,0.00005377803,0.00006077824,0.001814885],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001856089,"threshold_uncertainty_score":0.003690541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04348999876132689,"score_gpt":0.2023057016897356,"score_spread":0.1588157029284087,"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."}}