{"id":"W4309737893","doi":"10.36227/techrxiv.21552105.v1","title":"Machine Learning in Network Slicing - A Survey","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Slicing; Flexibility (engineering); Computer science; Key (lock); Architecture; Network architecture; Data science; Software engineering; World Wide Web; Computer network; Computer security","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":[0.003429757,0.001442648,0.001904527,0.002969496,0.0006446206,0.003328054,0.002588526,0.002119554,0.00404516],"category_scores_gemma":[0.008510828,0.0007958126,0.001276844,0.005869085,0.001180904,0.005692486,0.001778204,0.002632427,0.002013353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001439268,"about_ca_system_score_gemma":0.001686335,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002758696,"about_ca_topic_score_gemma":0.001480493,"domain_scores_codex":[0.9977429,0.0007137945,0.0002078291,0.0005423963,0.0006535713,0.0001396275],"domain_scores_gemma":[0.9932724,0.005063633,0.0002480082,0.000499548,0.0007627068,0.0001536368],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007154174,0.0001637226,0.003266072,0.003073369,0.0001534486,0.0001805941,0.0002296596,0.03890268,0.0006030529,0.07990056,0.02167258,0.8517828],"study_design_scores_gemma":[0.0000328116,0.0002618994,0.003633111,0.003450505,0.0002121089,0.001045016,0.0004854693,0.3078813,0.003078651,0.271813,0.407941,0.0001650712],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.005739358,0.5312176,0.4315421,0.005875435,0.001469272,0.0001489086,0.0005015337,0.000738033,0.02276775],"genre_scores_gemma":[0.1250459,0.7059138,0.1495833,0.002237935,0.006356642,0.0002751293,0.001747561,0.0004499725,0.008389779],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.00404516,"threshold_uncertainty_score":0.01813847,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03357641502406834,"score_gpt":0.2602531701786387,"score_spread":0.2266767551545704,"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."}}