{"id":"W2483718775","doi":"10.1109/bmsb.2016.7521933","title":"Performance evaluation of multiple-PLP based LDM systems for the next generation terrestrial broadcasting","year":2016,"lang":"en","type":"article","venue":"","topic":"Telecommunications and Broadcasting Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Broadcasting (networking); Multiplexing; Computer science; Division (mathematics); Time-division multiplexing; Layer (electronics); Physical layer; Variety (cybernetics); Frequency-division multiplexing; Telecommunications; Computer network; Orthogonal frequency-division multiplexing; Wireless; Materials science; Artificial intelligence; Mathematics; Nanotechnology","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":[0.0006889142,0.00007817581,0.00008579034,0.00005742473,0.0001075262,0.00002900435,0.0002170951,0.00005876299,0.00001092988],"category_scores_gemma":[0.0005445549,0.00004296419,0.00003366059,0.00009743909,0.00003348383,0.0001332113,0.00002094919,0.00004089138,0.000002851219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005911004,"about_ca_system_score_gemma":0.00002603518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002594793,"about_ca_topic_score_gemma":0.00001801447,"domain_scores_codex":[0.9993809,0.00002995044,0.0002401334,0.00008056848,0.0001510481,0.0001174262],"domain_scores_gemma":[0.9988813,0.0005507043,0.00006254434,0.0003794887,0.0001165711,0.00000940691],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009248023,0.00001185939,0.001064994,0.00002942954,0.00002307792,1.06181e-8,0.00002483166,0.261142,0.1145257,0.00007782833,0.0009091331,0.6221819],"study_design_scores_gemma":[0.0006897388,0.00003039517,0.0006967282,0.00005549251,0.00002608016,6.317421e-7,0.00005511564,0.9756513,0.02154274,0.000004318445,0.001178976,0.00006851725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8649135,0.0006323578,0.1325942,0.0001664381,0.0004293615,0.0006544172,0.000008397732,0.0003621359,0.0002391711],"genre_scores_gemma":[0.9938819,0.00008681361,0.005630917,0.000002439687,0.0001199876,0.0002381719,0.000007163344,0.0000129012,0.00001966819],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7145092,"threshold_uncertainty_score":0.1752029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1758764783305347,"score_gpt":0.2863808854744809,"score_spread":0.1105044071439463,"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."}}