{"id":"W1572887231","doi":"10.1109/tbc.2015.2432463","title":"Performance Study of Layered Division Multiplexing Based on SDR Platform","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Broadcasting","topic":"Telecommunications and Broadcasting Technologies","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Communications Research Centre Canada","funders":"Ministerio de Ciencia e Innovación; European Regional Development Fund; Euskal Herriko Unibertsitatea; Ministerio de Economía y Competitividad; Eusko Jaurlaritza","keywords":"Flexibility (engineering); Multiplexing; Broadcasting (networking); Computer science; Orthogonal frequency-division multiplexing; Frequency-division multiplexing; Channel (broadcasting); Division (mathematics); Cloud computing; Telecommunications; Electronic engineering; Computer network; Engineering; Operating system","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":[0.0002588108,0.0002210031,0.0002366591,0.0003726967,0.0002177691,0.00002874665,0.0003406883,0.0001037197,0.00001123222],"category_scores_gemma":[0.00004010489,0.000214373,0.00006046481,0.0005101461,0.00004062388,0.0001762035,0.000004533852,0.0004517875,0.00001975167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001196397,"about_ca_system_score_gemma":0.00002275191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004466538,"about_ca_topic_score_gemma":0.00002897988,"domain_scores_codex":[0.9988022,0.00002694434,0.0003975102,0.0002065431,0.0002951913,0.0002715851],"domain_scores_gemma":[0.9988924,0.0002409664,0.00007926702,0.0006303853,0.00008697446,0.00007005526],"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":[0.0000320171,0.0003419216,0.0004793011,0.00002913474,0.00001929166,0.00000138625,0.0005421045,0.865014,0.001299067,0.000001048013,0.00002003296,0.1322207],"study_design_scores_gemma":[0.001246734,0.0008549417,0.001035693,0.0001998575,0.00001280595,0.000003166772,0.001584012,0.9730943,0.0216617,0.000004791196,0.00007578631,0.0002262085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8244547,0.00001715958,0.1729575,0.00001184002,0.0002656692,0.0002602722,0.00001122879,0.0008990229,0.001122585],"genre_scores_gemma":[0.9932394,0.000007862257,0.006612489,0.000007716721,0.0000143509,0.0000516559,0.000002373322,0.0000447736,0.00001934646],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1687847,"threshold_uncertainty_score":0.8741876,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0705464669669233,"score_gpt":0.2576146856264903,"score_spread":0.187068218659567,"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."}}