{"id":"W4392349726","doi":"10.18280/ts.410135","title":"Electromagnetic Signal Anomaly Detection and Classification Methods Based on Deep Learning","year":2024,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Computational Physics and Python Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Anomaly detection; Artificial intelligence; Pattern recognition (psychology); SIGNAL (programming language); Anomaly (physics); Computer science; Deep learning; Detection theory; Machine learning; Physics; Telecommunications; Detector","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007943602,0.0007336236,0.0007221943,0.001222701,0.000309453,0.0006940243,0.001308905,0.0008214137,0.001095302],"category_scores_gemma":[0.001715384,0.000306889,0.0005484609,0.0009724679,0.0004520135,0.001178524,0.001037185,0.001412813,0.0004954398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006014311,"about_ca_system_score_gemma":0.0006734257,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00326065,"about_ca_topic_score_gemma":0.003648064,"domain_scores_codex":[0.9996376,0.00006155381,0.00002416174,0.00009046349,0.000134451,0.00005174209],"domain_scores_gemma":[0.9993719,0.0002099264,0.00009727827,0.00006182499,0.0002207331,0.0000382618],"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":[0.0001163712,0.0001880569,0.003034529,0.0001040045,0.00009081843,0.00009117945,0.00007756711,0.3305773,0.01455956,0.009952599,0.003676426,0.6375316],"study_design_scores_gemma":[0.000002023135,0.000008913917,0.000142058,0.000002710411,0.00000320674,0.00001103662,0.000002625688,0.9970029,0.0009650936,0.001550129,0.0003063017,0.000002990074],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01506216,0.0003713702,0.9824747,0.000210691,0.00005443455,0.00002692067,0.00004806582,0.00080523,0.0009464016],"genre_scores_gemma":[0.6406144,0.0008593653,0.3511547,0.0003620907,0.000200549,0.0001246384,0.0005529505,0.0001397414,0.005991609],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00326065,"threshold_uncertainty_score":0.006483316,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01926800168789362,"score_gpt":0.2869459104202148,"score_spread":0.2676779087323212,"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."}}