{"id":"W3188071926","doi":"10.1109/ojvt.2021.3097560","title":"Corrections to “Joint RAN Slicing and Computation Offloading for Autonomous Vehicular Networks: A Learning-Assisted Hierarchical Approach” [2021 doi: 10.1109/OJVT.2021.3089083]","year":2021,"lang":"en","type":"article","venue":"IEEE Open Journal of Vehicular Technology","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Slicing; Ran; Computer science; Joint (building); Computation; Artificial intelligence; Computer network; World Wide Web; Engineering; Algorithm","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":[0.004377165,0.001778301,0.001338357,0.002082382,0.003144463,0.004258971,0.003402869,0.007412477,0.04952275],"category_scores_gemma":[0.06402682,0.001067829,0.002114337,0.00204739,0.002244311,0.003252886,0.002188955,0.01011108,0.04366805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004172293,"about_ca_system_score_gemma":0.005611811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03060144,"about_ca_topic_score_gemma":0.0389035,"domain_scores_codex":[0.9910988,0.001333932,0.001003211,0.001069695,0.00495615,0.0005381646],"domain_scores_gemma":[0.9491494,0.007866807,0.002160773,0.002828807,0.03681357,0.001180714],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002647668,0.000005487416,0.00004475593,0.00004554879,0.000008278225,0.00008030501,0.00002146647,0.0001382019,0.0001551655,0.001991989,0.993024,0.004458366],"study_design_scores_gemma":[0.00003172053,0.00002629263,0.0004068414,0.0001357034,0.00002180322,0.0001784225,0.00005177541,0.001081552,0.0008041697,0.003926947,0.9932715,0.00006323439],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.000331125,0.001811594,0.01043894,0.1257121,0.8408608,0.00006920294,0.002595791,0.004940913,0.0132396],"genre_scores_gemma":[0.02766749,0.007418332,0.03097069,0.2006808,0.343824,0.0003145267,0.005883284,0.008393804,0.374847],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.04952275,"threshold_uncertainty_score":0.1656701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01697696465544884,"score_gpt":0.2519961420416432,"score_spread":0.2350191773861943,"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."}}