{"id":"W2065435360","doi":"10.1109/tit.2014.2353514","title":"The Streaming-DMT of Fading Channels","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Fading; Computer science; Decoding methods; Interleaving; Encoder; Algorithm; Coherence time; Channel (broadcasting); Multiplexing; Transmission (telecommunications); Coherence (philosophical gambling strategy); Coding (social sciences); Theoretical computer science; Computer network; Mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009095183,0.0007714425,0.0005256279,0.0003304487,0.000582775,0.0007499083,0.0007186641,0.0006895879,0.001630432],"category_scores_gemma":[0.003872096,0.0002879397,0.000327663,0.0007362917,0.001099044,0.001408286,0.000830583,0.0006459188,0.000204163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001196504,"about_ca_system_score_gemma":0.0008636679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00279308,"about_ca_topic_score_gemma":0.001652803,"domain_scores_codex":[0.9995221,0.0001617024,0.00001843579,0.00006706839,0.0001057266,0.0001249598],"domain_scores_gemma":[0.9983668,0.001082744,0.0001586973,0.0001372593,0.0001731863,0.00008141517],"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":[0.0002009911,0.00002999799,0.0008361647,0.0001726843,0.00002947586,0.0004730326,0.0001060931,0.7774763,0.00708973,0.1913133,0.001356018,0.02091619],"study_design_scores_gemma":[0.00001678185,0.00006224705,0.0001288691,0.00000848517,0.000008904109,0.0001669439,0.00001879774,0.9658895,0.001437861,0.03143628,0.000814454,0.00001084316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09457744,0.001449862,0.8893876,0.000572663,0.0001264571,0.00009608146,0.000294255,0.0001981644,0.0132974],"genre_scores_gemma":[0.9463226,0.001387101,0.0485454,0.0001122921,0.0001116238,0.00007185153,0.00009989314,0.00002726412,0.00332208],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00279308,"threshold_uncertainty_score":0.008681297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01529957168386576,"score_gpt":0.2397853005603596,"score_spread":0.2244857288764938,"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."}}