{"id":"W2964021047","doi":"10.3390/e21080732","title":"The Secret Key Capacity of a Class of Noisy Channels with Correlated Sources","year":2019,"lang":"en","type":"article","venue":"Entropy","topic":"Wireless Communication Security Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Horizon 2020 Framework Programme; Knut och Alice Wallenbergs Stiftelse; Stiftelsen för Strategisk Forskning; Stiftelsen för Strategisk Forskning; European Commission","keywords":"Channel (broadcasting); Key (lock); Binary number; Upper and lower bounds; Scheme (mathematics); Computer science; Transmission (telecommunications); Class (philosophy); Channel capacity; Topology (electrical circuits); Theoretical computer science; Mathematics; Telecommunications; Combinatorics; Computer security; Artificial intelligence; Arithmetic; Mathematical analysis","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":[0.002292133,0.0008956553,0.0009953509,0.001262995,0.0006403514,0.0023331,0.001296625,0.001147749,0.002556431],"category_scores_gemma":[0.01536497,0.0003675463,0.0004886367,0.001233477,0.003257244,0.00405182,0.002159143,0.001492685,0.0002924633],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001712522,"about_ca_system_score_gemma":0.001077876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008960501,"about_ca_topic_score_gemma":0.0003912249,"domain_scores_codex":[0.9984376,0.0004822104,0.00004392824,0.0001992335,0.000518383,0.000318688],"domain_scores_gemma":[0.9814827,0.01626994,0.0008014076,0.0006238691,0.0006437103,0.0001782909],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002323289,0.00005969625,0.0004979437,0.000391537,0.00005774934,0.0003116465,0.0002498411,0.634261,0.008673664,0.3384716,0.001258725,0.0155343],"study_design_scores_gemma":[0.00001125978,0.00003558714,0.0002081752,0.00005544007,0.00001546635,0.0001522595,0.00005835391,0.9077768,0.003535591,0.08720045,0.0009227502,0.00002780766],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.171679,0.002139333,0.8002187,0.0008677799,0.00008226815,0.00007301341,0.0003979148,0.0002230784,0.02431897],"genre_scores_gemma":[0.9813226,0.001651493,0.01408512,0.00008615261,0.00009818728,0.00008979857,0.0001367217,0.00006170099,0.002468246],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002556431,"threshold_uncertainty_score":0.01242524,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006972486365565015,"score_gpt":0.1904999745078866,"score_spread":0.1835274881423216,"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."}}