{"id":"W3139528444","doi":"10.1109/tcomm.2021.3067058","title":"Covert Surveillance via Proactive Eavesdropping Under Channel Uncertainty","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Wireless Communication Security Techniques","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Eavesdropping; Computer science; Covert; Transmitter; False alarm; Real-time computing; Transmission (telecommunications); Channel (broadcasting); Wireless; Decoding methods; Computer security; Computer network; Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001794882,0.0002354805,0.0002560304,0.000160851,0.0004958724,0.0000707394,0.0009496288,0.0001542058,0.000083178],"category_scores_gemma":[0.000008662532,0.0002878507,0.0001424814,0.0006878792,0.0001679368,0.0002448104,0.00001630258,0.0007843085,0.00006827485],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003139638,"about_ca_system_score_gemma":0.00008436624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008297979,"about_ca_topic_score_gemma":0.001001375,"domain_scores_codex":[0.9986501,0.0002655468,0.0003675142,0.0002367398,0.0002080149,0.000272057],"domain_scores_gemma":[0.9959732,0.0004299572,0.00005541322,0.003169377,0.0002716179,0.0001004219],"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":[0.00004391121,0.001915146,0.000024735,0.000136814,0.001184009,0.000007198789,0.004410078,0.8723015,0.06129596,0.008032965,0.002218327,0.04842936],"study_design_scores_gemma":[0.0009966535,0.00008184895,0.0005919508,0.0002278377,0.00009555743,0.00009307432,0.001507036,0.7008518,0.2616331,0.006415911,0.02609112,0.001414132],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002107999,0.0007356996,0.9829727,0.002110413,0.0002374612,0.0003278863,0.0001055248,0.0013981,0.01000418],"genre_scores_gemma":[0.988775,0.00359179,0.006692678,0.0002033422,0.00001589303,0.0003402735,0.0000663375,0.00006470104,0.0002500373],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.986667,"threshold_uncertainty_score":0.9999574,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0307907530839494,"score_gpt":0.2654940751060125,"score_spread":0.2347033220220631,"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."}}