{"id":"W4226093231","doi":"10.1109/tvt.2022.3165172","title":"Intelligence Networking for Autonomous Driving in Beyond 5G Networks With Multi-Access Edge Computing","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Upload; Enhanced Data Rates for GSM Evolution; Edge computing; Artificial neural network; Curse of dimensionality; Artificial intelligence; Scheme (mathematics); Distributed computing; Real-time computing","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002750713,0.0005352004,0.0003392572,0.0003841578,0.00062669,0.0007350312,0.0008367862,0.0005459603,0.001243054],"category_scores_gemma":[0.0005703714,0.0001296945,0.0002649942,0.0004005691,0.0003241558,0.001627723,0.001082887,0.0007737926,0.0003193686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005236397,"about_ca_system_score_gemma":0.0005309916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006880228,"about_ca_topic_score_gemma":0.01133158,"domain_scores_codex":[0.9997988,0.00003630197,0.000008646015,0.0000529159,0.00004097468,0.00006229181],"domain_scores_gemma":[0.9998264,0.0000411889,0.00001467388,0.00003778279,0.00006027892,0.0000197104],"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":[0.0004722226,0.0003518422,0.008786037,0.0001981923,0.00009938742,0.001098452,0.0004166543,0.3173728,0.02313407,0.06110356,0.03293599,0.5540308],"study_design_scores_gemma":[0.000008555392,0.0000680481,0.0007569353,0.00001169348,0.00001763776,0.000140264,0.00007559722,0.9735785,0.002961119,0.01493171,0.007434511,0.00001559205],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1417762,0.002281155,0.823669,0.002778491,0.0006295777,0.000146364,0.0003711848,0.003010898,0.02533722],"genre_scores_gemma":[0.9595352,0.0003719297,0.03710716,0.0003234859,0.00006459386,0.00004608103,0.0002291281,0.00002959602,0.002292783],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006880228,"threshold_uncertainty_score":0.01368034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0211958398522331,"score_gpt":0.2790304076852188,"score_spread":0.2578345678329857,"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."}}