{"id":"W4306177155","doi":"10.48550/arxiv.2210.05874","title":"Multi-Content Time-Series Popularity Prediction with Multiple-Model Transformers in MEC Networks","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministère de la Défense Nationale","keywords":"Computer science; Cache; Popularity; Boosting (machine learning); Mobile device; Generalization; Enhanced Data Rates for GSM Evolution; Transformer; Artificial intelligence; Data mining; Computer network; World Wide Web; Engineering; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003529781,0.0003931619,0.0004202302,0.0003382831,0.0002693835,0.0001222133,0.001258315,0.0002486118,0.00001553136],"category_scores_gemma":[0.00001496603,0.000428971,0.0002425419,0.0005157503,0.00009396121,0.0007922342,0.0009109005,0.001237385,0.00000674877],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005102673,"about_ca_system_score_gemma":0.000196541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00170026,"about_ca_topic_score_gemma":0.001345976,"domain_scores_codex":[0.9976693,0.000191187,0.0002818319,0.001238001,0.0001625827,0.0004571158],"domain_scores_gemma":[0.9986969,0.00005168738,0.0001848002,0.0008187527,0.00009495956,0.0001528994],"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":[0.0002431521,0.0001924512,0.0250084,0.00002815904,0.00008262741,0.0002420231,0.0003007926,0.9711105,0.00005955088,0.002348031,0.00003338961,0.000350976],"study_design_scores_gemma":[0.001415492,0.0001169764,0.004841412,0.00008372221,0.00006092492,0.000008566979,0.0001889573,0.9924018,0.000008420579,0.0003847523,0.00004256127,0.0004463541],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2750286,0.00006212389,0.723483,0.00008158765,0.000338679,0.0004280313,0.00004877471,0.0002862331,0.0002429758],"genre_scores_gemma":[0.9947741,0.0001702834,0.002406484,0.00008467345,0.00002761821,0.000008176759,0.00009220983,0.0000248235,0.002411602],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7210765,"threshold_uncertainty_score":0.9998162,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07717167065124027,"score_gpt":0.1740234597411041,"score_spread":0.09685178908986387,"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."}}