{"id":"W2996804334","doi":"10.2514/6.2020-1194","title":"Partial Label Learning of RF Emitters with LSTMs","year":2020,"lang":"en","type":"article","venue":"AIAA Scitech 2020 Forum","topic":"Wireless Signal Modulation Classification","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Discriminator; Computer science; Radio frequency; Agile software development; Exploit; Radar; Artificial intelligence; Frequency modulation; Identification (biology); Modulation (music); Recurrent neural network; Class (philosophy); Artificial neural network; Telecommunications; Detector","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":[0.0001320148,0.000121937,0.0001639893,0.00005130503,0.0001025723,0.00007525819,0.0005767571,0.00005522528,0.00001676941],"category_scores_gemma":[0.00006867221,0.000108957,0.00003818225,0.000830731,0.00006527526,0.0004714709,0.000167155,0.0001872088,0.00006665449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002326745,"about_ca_system_score_gemma":0.00006480305,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001227425,"about_ca_topic_score_gemma":0.000002415792,"domain_scores_codex":[0.998657,0.00004932689,0.0002484407,0.0003899336,0.0004010079,0.0002543045],"domain_scores_gemma":[0.999227,0.00006525688,0.0001952182,0.0002818395,0.0001012186,0.0001294673],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002197328,0.0002414059,0.100721,0.0001643451,0.0001604083,0.00005835504,0.005285737,0.02981581,0.4861173,0.2401219,0.01157563,0.1255184],"study_design_scores_gemma":[0.001042037,0.0007206921,0.005948862,0.00005945032,0.00001508127,0.000009531517,0.0002501845,0.8930078,0.08439661,0.0006324599,0.01353493,0.0003824071],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06729654,0.00002954184,0.9027891,0.02882054,0.00009107464,0.0001717122,0.000001878324,0.0002560516,0.0005435997],"genre_scores_gemma":[0.9789595,0.000003090422,0.01991168,0.0009657298,0.00005773245,0.00001376795,0.000005531278,0.00001322362,0.00006974606],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9116629,"threshold_uncertainty_score":0.4443138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02100889655016636,"score_gpt":0.2324766571328138,"score_spread":0.2114677605826475,"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."}}