{"id":"W2886341309","doi":"10.1109/bmsb.2018.8436876","title":"ATSC 3.0 Physical Layer Modulation and Coding Performance Analysis","year":2018,"lang":"en","type":"article","venue":"","topic":"Telecommunications and Broadcasting Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"PHY; Physical layer; Computer science; Low-density parity-check code; Coding (social sciences); Phase-shift keying; Forward error correction; Throughput; Field trial; Coding gain; Modulation (music); Decoding methods; Electronic engineering; Bit error rate; Telecommunications; Wireless; Engineering","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":[0.0008252162,0.0006012016,0.0002825554,0.0006430295,0.0003904239,0.000461101,0.0004054447,0.0003580075,0.002449573],"category_scores_gemma":[0.001711356,0.0001045695,0.0002925349,0.0006101781,0.0003122125,0.0003563522,0.0002118837,0.0003160962,0.0005887816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001179744,"about_ca_system_score_gemma":0.0009966072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009737249,"about_ca_topic_score_gemma":0.005265241,"domain_scores_codex":[0.9994107,0.000111341,0.0000256367,0.00004990821,0.0002986436,0.0001037076],"domain_scores_gemma":[0.9982501,0.0003524724,0.0001462468,0.0001734717,0.001036416,0.00004128307],"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":[0.0008682703,0.0002686389,0.01876263,0.0002309868,0.0001546879,0.0003987792,0.0001243841,0.7494563,0.1304023,0.007629599,0.008873248,0.08283003],"study_design_scores_gemma":[0.00004614776,0.0007064798,0.01150203,0.00002388732,0.0000576422,0.0003050729,0.00006411041,0.9074812,0.07291876,0.001734469,0.005102527,0.00005770143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7880549,0.0005160572,0.1716135,0.0003940082,0.00009879463,0.0004209363,0.002377928,0.002962429,0.03356142],"genre_scores_gemma":[0.9820207,0.0001440076,0.01433638,0.00009079477,0.00001088637,0.00009045002,0.001293809,0.0001175863,0.001895464],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009737249,"threshold_uncertainty_score":0.01936114,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02056714999030297,"score_gpt":0.2422554410219258,"score_spread":0.2216882910316229,"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."}}