{"id":"W2112352771","doi":"10.1109/mwscas.1996.593231","title":"A neural network-based detection thresholding scheme for active sonar signal tracking","year":2002,"lang":"en","type":"article","venue":"","topic":"Radar Systems and Signal Processing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Thresholding; Sonar; Computer science; Constant false alarm rate; Marine mammals and sonar; False alarm; Artificial intelligence; Energy (signal processing); Artificial neural network; Noise (video); Underwater; SIGNAL (programming language); Tracking (education); Computer vision; Signal-to-noise ratio (imaging); Real-time computing; Mathematics; Telecommunications; Statistics; Image (mathematics); Geography","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":[0.000103019,0.0001418618,0.0001503644,0.00004946751,0.0001814135,0.00008800017,0.00006732838,0.00007904325,0.0001222511],"category_scores_gemma":[0.000004254836,0.0001355835,0.0000849568,0.0001439499,0.00001067489,0.0002689338,0.000004784609,0.0001333582,0.000007185112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006094929,"about_ca_system_score_gemma":0.00000380034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007272048,"about_ca_topic_score_gemma":0.00001848865,"domain_scores_codex":[0.999209,0.000009970894,0.0001863218,0.0001564473,0.0001170321,0.000321176],"domain_scores_gemma":[0.999738,0.00007051712,0.00003170335,0.00006669309,0.00003750505,0.00005555804],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002457777,0.00001433276,0.0001754616,0.0001590861,0.00004043572,0.000005144042,0.0001631377,0.6363215,0.05724012,0.00006309069,0.0009789363,0.3048142],"study_design_scores_gemma":[0.0003374714,0.00004021027,0.00008167777,0.00004893702,0.000008415596,0.000006448566,0.00004566255,0.9626629,0.03493518,0.0000580165,0.001600334,0.0001747845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2534294,0.0008385736,0.7413545,0.0000450917,0.0002931187,0.0003289433,0.000003029422,0.0006244878,0.003082855],"genre_scores_gemma":[0.993986,0.000001748062,0.005303624,0.00005378834,0.0004963772,0.00003507103,0.000001511423,0.0000418007,0.00008007324],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7405567,"threshold_uncertainty_score":0.5528935,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03216638164084516,"score_gpt":0.2183682931287694,"score_spread":0.1862019114879242,"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."}}