{"id":"W4385695998","doi":"10.1109/jiot.2023.3303452","title":"Meta Relational Learning-Based Service-Tailored VNF Deployment for B5G Network Slice","year":2023,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Software deployment; Server; Distributed computing; Virtual network; Task (project management); Slicing; Service (business); Relation (database); Artificial intelligence; Computer network; Data mining; Software engineering; World Wide Web; Systems 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":[0.0008076794,0.0009684441,0.0008618844,0.000447234,0.000465335,0.0007592613,0.001378945,0.001035811,0.002300708],"category_scores_gemma":[0.002607284,0.0003702097,0.0005667944,0.0005044251,0.0006958984,0.002056721,0.001447411,0.001160434,0.0002724625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001398793,"about_ca_system_score_gemma":0.001271469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01271613,"about_ca_topic_score_gemma":0.01086343,"domain_scores_codex":[0.999552,0.0001166758,0.00001889193,0.0001124969,0.00007028588,0.0001295694],"domain_scores_gemma":[0.9992636,0.0003738926,0.00006677288,0.0000977923,0.0001086779,0.00008926776],"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":[0.00006893103,0.00005122572,0.0009525095,0.00003744846,0.00001775921,0.00006558441,0.00006881851,0.9574628,0.001088189,0.004198487,0.001236484,0.03475175],"study_design_scores_gemma":[0.000004072554,0.00001466407,0.00006230038,0.000002209016,0.000002906362,0.00001003172,0.00001345743,0.9979548,0.0002020015,0.001590995,0.00014041,0.000002019073],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1005175,0.0005578664,0.8925349,0.0005990918,0.00006406494,0.00009632616,0.0001401209,0.001370412,0.004119799],"genre_scores_gemma":[0.9207945,0.0001780255,0.07685834,0.0001730022,0.00002314911,0.00008740382,0.0002304001,0.00007554928,0.001579524],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01271613,"threshold_uncertainty_score":0.02528423,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06883153905255818,"score_gpt":0.2701142392520204,"score_spread":0.2012827001994622,"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."}}