{"id":"W4389576759","doi":"10.1109/ccpqt60491.2023.00044","title":"Physical Layer Security Performance Prediction Based on Deep Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Wireless Signal Modulation Classification","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Eavesdropping; Computer science; Channel (broadcasting); Train; Physical layer; Computer network; Wireless; Information security; Layer (electronics); Computer security; Telecommunications","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":[0.0006034375,0.001144514,0.0005191512,0.0005021031,0.0002173945,0.000667158,0.0007147269,0.0005495397,0.001476054],"category_scores_gemma":[0.002211139,0.0002849824,0.0002710261,0.0002886213,0.0003967278,0.001295356,0.000715224,0.00140139,0.0004396285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006325274,"about_ca_system_score_gemma":0.0006414928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002866619,"about_ca_topic_score_gemma":0.004431766,"domain_scores_codex":[0.9996762,0.00005278478,0.00001568033,0.00005520524,0.0001161563,0.00008388769],"domain_scores_gemma":[0.9992787,0.0003258612,0.00009128815,0.00007652898,0.0001937636,0.00003381604],"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":[0.0002073071,0.0001713336,0.004348188,0.00006841031,0.00007596055,0.0001282747,0.00003291353,0.8279845,0.01486327,0.003639323,0.002224226,0.1462563],"study_design_scores_gemma":[0.000001106693,0.00001226374,0.0001826185,0.000002160411,0.000003193509,0.000007223444,0.00000153004,0.9976491,0.001356456,0.0007114006,0.00007050957,0.000002381135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1492852,0.0009024664,0.8419711,0.0006876808,0.0001479953,0.00004317626,0.0002408658,0.001976966,0.004744445],"genre_scores_gemma":[0.970507,0.0003083332,0.02651685,0.0001136845,0.00003742797,0.0000248474,0.0001842812,0.0000356815,0.00227191],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002866619,"threshold_uncertainty_score":0.005699873,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02311425501844901,"score_gpt":0.2498686476668594,"score_spread":0.2267543926484104,"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."}}