{"id":"W2891286892","doi":"10.1109/tmm.2018.2870521","title":"Content Popularity Prediction Towards Location-Aware Mobile Edge Caching","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":197,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Backhaul (telecommunications); Cache; Algorithm; Exploit; Regret; Data mining; Machine learning; Base station; Computer network","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002682341,0.0002049572,0.0001824934,0.0001982564,0.0004898225,0.0001309475,0.0004915596,0.0001263424,0.00004649904],"category_scores_gemma":[0.00001387765,0.0001988822,0.0001353118,0.0003671496,0.00009900171,0.0005715718,0.000005306003,0.0003729023,0.0002851179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001603243,"about_ca_system_score_gemma":0.000107811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001144676,"about_ca_topic_score_gemma":0.0002093686,"domain_scores_codex":[0.9983848,0.000102766,0.0003072024,0.0005044991,0.0003924887,0.0003082873],"domain_scores_gemma":[0.9987272,0.0000754034,0.00007518091,0.0006331541,0.0003154371,0.0001736497],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001202007,0.001216854,0.0005004042,0.00005081388,0.0001928415,0.00002257006,0.004942507,0.01908036,0.01561162,0.0001789613,0.001423627,0.9566593],"study_design_scores_gemma":[0.001081529,0.0005506044,0.002828256,0.0000960065,0.00004809955,0.00003311349,0.0002060168,0.9702821,0.02343781,0.00005327926,0.001003991,0.0003791375],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06034376,0.00004760873,0.9344415,0.0002313578,0.003847225,0.0003266883,0.00004638801,0.0005165931,0.0001988924],"genre_scores_gemma":[0.9956813,0.00001880906,0.002942033,0.0002997317,0.0002638731,0.0001142877,0.000008881515,0.00001671198,0.0006543374],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9562801,"threshold_uncertainty_score":0.8110183,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03979363620046616,"score_gpt":0.2607314566692937,"score_spread":0.2209378204688275,"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."}}