{"id":"W3007600769","doi":"10.1109/tcomm.2020.2974738","title":"A Deep Reinforcement Learning-Based Transcoder Selection Framework for Blockchain-Enabled Wireless D2D Transcoding","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Carleton University","funders":"National Natural Science Foundation of China","keywords":"Transcoding; Computer science; Reinforcement learning; Quality of service; Scheduling (production processes); Distributed computing; Real-time computing; Computer network; Artificial intelligence; 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":[0.0007623415,0.0005363802,0.0007859655,0.0002131559,0.0002741997,0.0005493943,0.001082592,0.0007189106,0.001584433],"category_scores_gemma":[0.001419739,0.0002827063,0.0003368213,0.0002445278,0.0006864187,0.000665032,0.0008844323,0.001026668,0.0001803467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007982281,"about_ca_system_score_gemma":0.001079422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006988518,"about_ca_topic_score_gemma":0.006278863,"domain_scores_codex":[0.9996921,0.00007742124,0.00001345051,0.00007012853,0.00008822814,0.00005870376],"domain_scores_gemma":[0.999476,0.0002518091,0.00006211736,0.00003310927,0.0001268091,0.00005022398],"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":[0.00004601482,0.00004039508,0.0004389265,0.00003229132,0.00002023968,0.00009597001,0.00004137637,0.9622664,0.002400952,0.006797831,0.0007049449,0.02711466],"study_design_scores_gemma":[0.000002487423,0.000006850275,0.00001859361,9.53418e-7,0.000001449388,0.00000442866,0.00000144509,0.999028,0.0001341402,0.0007214238,0.00007909995,0.000001239635],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02005565,0.000266833,0.9769552,0.0001942351,0.00003110493,0.00002911656,0.00003434396,0.00021014,0.002223477],"genre_scores_gemma":[0.9369428,0.0001785762,0.05943391,0.0001168492,0.00002808349,0.00007599469,0.00005960877,0.00003668945,0.003127453],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006988518,"threshold_uncertainty_score":0.01389569,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02848112148239362,"score_gpt":0.2623332780261198,"score_spread":0.2338521565437262,"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."}}