{"id":"W2083353654","doi":"10.1109/isit.2013.6620252","title":"Time-asynchronous Gaussian multiple access channel with correlated sources","year":2013,"lang":"en","type":"article","venue":"","topic":"Wireless Communication Security Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Upper and lower bounds; Gaussian; Entropy (arrow of time); Offset (computer science); Encoder; Coding (social sciences); Channel code; Channel (broadcasting); Algorithm; Computer science; Variable-length code; Source code; Asymptotically optimal algorithm; Combinatorics; Mathematics; Discrete mathematics; Decoding methods; Physics; Statistics; Telecommunications; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00004733011,0.0001653508,0.0001619503,0.0001009065,0.00006860877,0.0001327229,0.0005386772,0.0001000427,0.00102932],"category_scores_gemma":[0.000008393764,0.000135206,0.00002626751,0.0002028713,0.00006773598,0.0004841932,0.0001063126,0.0001984873,0.0007421112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004395211,"about_ca_system_score_gemma":0.000009225227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003493408,"about_ca_topic_score_gemma":0.00008461243,"domain_scores_codex":[0.9993253,0.00002110834,0.000173397,0.0001323842,0.0001168532,0.0002309757],"domain_scores_gemma":[0.9992318,0.00006293786,0.00003170614,0.0005246839,0.00006123124,0.00008765903],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001528296,0.001833183,0.05371996,0.001178103,0.002237208,0.00006443887,0.02788867,0.3512441,0.06878356,0.004984617,0.3531231,0.1347903],"study_design_scores_gemma":[0.0004858203,0.0000636826,0.007384757,0.00008279439,0.00001298724,0.0000173759,0.0001977985,0.9540678,0.03367334,0.0002096796,0.003260186,0.0005437632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8678868,0.0003319892,0.05397044,0.0004985399,0.00006859864,0.0009573998,0.000005743589,0.007193995,0.06908646],"genre_scores_gemma":[0.9961512,0.00005266108,0.003048144,0.00007653475,0.00001850315,0.0001537997,0.00002194613,0.00005388745,0.0004233294],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6028237,"threshold_uncertainty_score":0.9998839,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009371902492176308,"score_gpt":0.2103039620248973,"score_spread":0.200932059532721,"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."}}