{"id":"W2047986257","doi":"10.1109/isit.2013.6620610","title":"Real-time streaming of Gauss-Markov sources over sliding window burst-erasure channels","year":2013,"lang":"en","type":"article","venue":"","topic":"Wireless Communication Security Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Erasure; Encoder; Sliding window protocol; Upper and lower bounds; Binary erasure channel; Computer science; Algorithm; Gaussian; Markov process; Erasure code; Markov chain; Decoding methods; Mathematics; Channel (broadcasting); Channel capacity; Window (computing); Telecommunications; Physics; Statistics","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":[0.0001807065,0.0001909239,0.0002813055,0.0001578908,0.00005332708,0.00006336749,0.0004085768,0.000136001,0.001075745],"category_scores_gemma":[0.00002897468,0.0001883459,0.00007568324,0.0002021837,0.00003587593,0.0003806345,0.0001164075,0.000174096,0.00007802665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005115524,"about_ca_system_score_gemma":0.000008489365,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006049974,"about_ca_topic_score_gemma":0.000009534281,"domain_scores_codex":[0.998988,0.00004250698,0.0003450111,0.0001498303,0.0002112523,0.0002633768],"domain_scores_gemma":[0.9989672,0.0001616054,0.0000713816,0.0006294717,0.00009466101,0.00007572137],"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":[0.00000480097,0.00008362497,0.004501899,0.0002346215,0.000168762,0.000001684575,0.002878623,0.0018237,0.9654785,0.003195963,0.01071357,0.01091426],"study_design_scores_gemma":[0.0005908197,0.00008796616,0.01899546,0.0004772944,0.00004133148,0.000008876009,0.000816069,0.1450989,0.8277119,0.001866227,0.003266685,0.001038519],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9677277,0.0001934733,0.001524699,0.00009938561,0.00005696873,0.0003176382,0.000006102193,0.001334025,0.02874001],"genre_scores_gemma":[0.991058,0.0002834165,0.007985236,0.00001684501,0.00005499595,0.00005397603,0.00001300928,0.00005473811,0.0004797913],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1432751,"threshold_uncertainty_score":0.9998374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009266078322678863,"score_gpt":0.2236293288649856,"score_spread":0.2143632505423068,"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."}}