{"id":"W2294088060","doi":"10.1109/tbc.2015.2505411","title":"LDM Core Services Performance in ATSC 3.0","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Broadcasting","topic":"Telecommunications and Broadcasting Technologies","field":"Engineering","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"Communications Research Centre Canada","funders":"European Regional Development Fund; Euskal Herriko Unibertsitatea; Eusko Jaurlaritza","keywords":"Digital television; Computer science; Broadcasting (networking); Digital Video Broadcasting; Digital broadcasting; Multiplexing; Digital audio broadcasting; Channel (broadcasting); Enhanced Data Rates for GSM Evolution; Electronic engineering; Telecommunications; Computer network; 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":[0.0008320723,0.0006827713,0.0005486628,0.0007325826,0.0007431213,0.0007464528,0.0005240706,0.0006983092,0.001594667],"category_scores_gemma":[0.002556495,0.0001023536,0.0002356758,0.0008240439,0.0005195056,0.0007558726,0.0008530282,0.0004010769,0.000453965],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001739921,"about_ca_system_score_gemma":0.0009117767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01218442,"about_ca_topic_score_gemma":0.007215757,"domain_scores_codex":[0.9989759,0.0001281067,0.0000392663,0.0001226902,0.0003503378,0.0003836589],"domain_scores_gemma":[0.9979772,0.0004920926,0.000146793,0.0001657982,0.0009918142,0.0002262296],"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":[0.01070135,0.0009905127,0.05569898,0.0003922305,0.0001867446,0.001573645,0.0009146215,0.326589,0.3606608,0.007053174,0.01044295,0.224796],"study_design_scores_gemma":[0.0001088383,0.002199393,0.0253278,0.00004463435,0.00008836364,0.000962851,0.0005320947,0.792404,0.1737811,0.001554352,0.002904916,0.00009169351],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9885851,0.0002366038,0.00594272,0.000106536,0.00002055735,0.00002368195,0.00018617,0.0006614478,0.004237191],"genre_scores_gemma":[0.9982912,0.00004853135,0.001053797,0.00004396975,0.00000364621,0.000007232161,0.000160793,0.00002332888,0.000367445],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01218442,"threshold_uncertainty_score":0.02422696,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02325962740394934,"score_gpt":0.2220131720675952,"score_spread":0.1987535446636459,"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."}}