{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001159635,0.000848164,0.001002043,0.0005502622,0.0003464002,0.0008318179,0.001633633,0.0009152204,0.001151012],"category_scores_gemma":[0.00455291,0.0004895311,0.0007265693,0.0007985564,0.0006250142,0.001534664,0.0008317394,0.001241379,0.000262991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001575079,"about_ca_system_score_gemma":0.0008002952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02559878,"about_ca_topic_score_gemma":0.01675126,"domain_scores_codex":[0.9995944,0.0001187752,0.00002048039,0.0001092329,0.00007606615,0.00008108938],"domain_scores_gemma":[0.9977991,0.001502683,0.0001999508,0.0001053246,0.0003227798,0.0000701457],"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":[0.00007206244,0.00001964254,0.001174535,0.0000311966,0.00002386346,0.00007227925,0.00003274112,0.9779664,0.0005066444,0.005675886,0.0004920688,0.01393256],"study_design_scores_gemma":[9.347845e-7,0.0000035577,0.00003844604,8.907773e-7,0.000002215034,0.000004887184,0.000002210055,0.9992051,0.00004267834,0.0006690886,0.00002865009,0.000001401713],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08226902,0.000987635,0.9130923,0.0004804836,0.00006790431,0.00004438176,0.0002517262,0.0006243977,0.0021821],"genre_scores_gemma":[0.9748555,0.0004672959,0.02213657,0.0001177084,0.00004314987,0.00004601703,0.000252219,0.00004215638,0.002039457],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02559878,"threshold_uncertainty_score":0.05089951,"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."}}