{"id":"W2565579085","doi":"10.1109/eusipco.2016.7760481","title":"Detection of modern communication signals using frequency domain morphological filtering","year":2016,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Federation for the Humanities and Social Sciences","keywords":"Computer science; Narrowband; Drone; UMTS frequency bands; Frequency domain; False alarm; Wireless; SIGNAL (programming language); Real-time computing; Intrusion detection system; Sensitivity (control systems); Electronic engineering; Telecommunications; Artificial intelligence; Engineering; Computer vision","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0002285128,0.00006301805,0.00009088675,0.00005432298,0.00008346923,0.00003659079,0.0004324405,0.00004338727,0.00002778224],"category_scores_gemma":[0.00003064343,0.00004009414,0.00003189039,0.0001435279,0.0000440793,0.0005151269,0.0001506672,0.00003861486,0.000006495524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003368764,"about_ca_system_score_gemma":0.00001860983,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001884061,"about_ca_topic_score_gemma":0.000004929063,"domain_scores_codex":[0.9993568,0.00005505779,0.0001761495,0.0001590123,0.0001203136,0.0001326384],"domain_scores_gemma":[0.9993951,0.00006433047,0.0000997465,0.0003510308,0.00005702526,0.00003274244],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000001345298,0.00001042204,0.0001538486,0.000002859874,0.000002112277,0.000001431627,0.00005740162,0.00001551605,0.9082364,0.0003763544,0.000001183821,0.09114116],"study_design_scores_gemma":[0.0001323043,0.00002771766,0.0003428302,0.00004641883,0.000001215727,0.00002693362,0.000006983608,0.003692328,0.8992139,0.0964344,0.000005490233,0.00006947496],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4057354,0.00008766263,0.5933236,0.000124189,0.00001920011,0.00002590878,2.454737e-7,0.00005408205,0.0006296823],"genre_scores_gemma":[0.6557314,0.000006595025,0.3441996,0.0000388435,0.00000793246,0.000001597372,7.933829e-8,0.00000221403,0.00001171073],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.249996,"threshold_uncertainty_score":0.1634991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03917044803663932,"score_gpt":0.2674487763184434,"score_spread":0.2282783282818041,"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."}}