{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006534758,0.001108094,0.0006829684,0.0004602307,0.000256443,0.0008137911,0.001367759,0.001215855,0.002248141],"category_scores_gemma":[0.002654657,0.0003183399,0.0006558485,0.0006246361,0.0003773995,0.001646121,0.0009725004,0.001981991,0.001367319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006468515,"about_ca_system_score_gemma":0.0005628743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003538328,"about_ca_topic_score_gemma":0.005315719,"domain_scores_codex":[0.9996465,0.00008852153,0.00001968071,0.0001260995,0.00006124309,0.00005788688],"domain_scores_gemma":[0.9991061,0.000388444,0.00009470528,0.0001419476,0.0002277243,0.00004103726],"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":[0.0004050329,0.0002234562,0.002209231,0.0002138647,0.0001216775,0.0001639068,0.0001790499,0.3974605,0.0196813,0.007048493,0.012915,0.5593785],"study_design_scores_gemma":[0.000005675046,0.00002694719,0.0001495052,0.000009424532,0.00000746909,0.00000889248,0.000008572481,0.9934875,0.001976717,0.003691825,0.0006215724,0.000005807493],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.100348,0.001339852,0.8860049,0.001030306,0.0004582344,0.00007072689,0.001100655,0.00556373,0.004083629],"genre_scores_gemma":[0.8445458,0.0004758091,0.1423248,0.0005006326,0.0003061364,0.000133293,0.003207179,0.0002625066,0.008243757],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003538328,"threshold_uncertainty_score":0.007520795,"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."}}