{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001205317,0.00008231248,0.0001570687,0.00003648634,0.00002484729,0.00001008824,0.0003008329,0.00005715195,0.00003360911],"category_scores_gemma":[0.00001370367,0.00005800524,0.00003428546,0.0001285891,0.000086821,0.00005190954,0.00003824951,0.0001658435,0.00001235628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002099231,"about_ca_system_score_gemma":0.000008582307,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005053479,"about_ca_topic_score_gemma":0.00001321363,"domain_scores_codex":[0.9994571,0.00003946262,0.0001813338,0.00006236832,0.0001435221,0.0001162722],"domain_scores_gemma":[0.9992319,0.0001112048,0.00008319334,0.0004838419,0.00006725221,0.00002258593],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003730327,0.0002921514,0.06006794,0.0008087008,0.001007473,0.00000314608,0.03234933,0.05888012,0.5774657,0.2612986,0.004921994,0.002531863],"study_design_scores_gemma":[0.001074618,0.0003160667,0.004611404,0.0003219731,0.00003932338,0.000008772568,0.0007869859,0.1598883,0.792729,0.001283437,0.03855455,0.0003856233],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952382,0.0002473149,0.002165688,0.00007823969,0.0000705115,0.0001897483,0.000007976848,0.0001764581,0.001825882],"genre_scores_gemma":[0.9990559,0.0001153547,0.000713501,0.000007131497,0.000009541497,0.00001054837,0.000004026665,0.00001802562,0.00006593126],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2600152,"threshold_uncertainty_score":0.2365385,"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."}}