{"id":"W3005801438","doi":"10.1109/tbc.2020.2968730","title":"Deep Learning-Based Resource Allocation for 5G Broadband TV Service","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Broadcasting","topic":"Telecommunications and Broadcasting Technologies","field":"Engineering","cited_by":90,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"National Natural Science Foundation of China","keywords":"Computer science; Multicast; Unicast; Computer network; Resource allocation; Quality of service; Multi-frequency network; Wireless broadband; Wireless network; Wireless; Telecommunications; Heterogeneous network","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":[0.0004387083,0.0006425067,0.0005283839,0.0002982282,0.0003017261,0.0005553373,0.0009844698,0.0006528551,0.001467604],"category_scores_gemma":[0.0008664865,0.0002481224,0.0003690153,0.0004225528,0.000360173,0.001085611,0.0006418781,0.001119318,0.0002948267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00103124,"about_ca_system_score_gemma":0.001146968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01457584,"about_ca_topic_score_gemma":0.01226328,"domain_scores_codex":[0.999707,0.00005340905,0.0000141601,0.00007346,0.00006344157,0.00008848865],"domain_scores_gemma":[0.999833,0.00006060182,0.000021883,0.0000166567,0.00005003387,0.00001775128],"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":[0.0001622004,0.0001856787,0.001049897,0.00006428264,0.00004172879,0.00008119696,0.00006038813,0.7946832,0.007035293,0.004095533,0.003865979,0.1886746],"study_design_scores_gemma":[0.00000239028,0.000009379108,0.00004381475,0.000001146205,0.000002275021,0.000002750293,0.000003278837,0.9987968,0.0004855337,0.0005395263,0.0001113896,0.000001761115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08690134,0.001299096,0.9027339,0.0007806659,0.0001204225,0.0000597319,0.0001478892,0.002144209,0.005812803],"genre_scores_gemma":[0.9501045,0.0002957683,0.04643308,0.0002591286,0.00003610074,0.00006633373,0.0002062316,0.00006078348,0.002537942],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01457584,"threshold_uncertainty_score":0.02898198,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02980484242509043,"score_gpt":0.231484700483069,"score_spread":0.2016798580579786,"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."}}