{"id":"W4315783404","doi":"10.1109/icnsc55942.2022.10004115","title":"Content Placement in a Cluster-Centric Mobile Edge Caching Network","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada; Concordia University","funders":"","keywords":"Computer science; Computer network; Cache; Enhanced Data Rates for GSM Evolution; Latency (audio); Focus (optics); Distributed computing; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00117976,0.0003336441,0.0004145287,0.0003092059,0.0005725291,0.0004392498,0.0007185105,0.00006663874,0.00006448706],"category_scores_gemma":[0.00002520929,0.0003537703,0.0001460755,0.000369745,0.00005032112,0.0001999027,0.000425914,0.0008485993,0.00001098187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003871607,"about_ca_system_score_gemma":0.0001179801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000615011,"about_ca_topic_score_gemma":0.0002423099,"domain_scores_codex":[0.9965914,0.000528766,0.0005866816,0.0008212118,0.0008213706,0.0006506052],"domain_scores_gemma":[0.9986596,0.0003390276,0.0003143697,0.0004155378,0.0001257283,0.0001458036],"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":[0.001235473,0.0005251052,0.005189413,0.00001887469,0.0004153041,0.0006116981,0.00197076,0.7548373,0.002478315,0.02881502,0.01004506,0.1938577],"study_design_scores_gemma":[0.002804446,0.0003282521,0.0003541173,0.0001303084,0.000019295,0.0001117584,0.0003873168,0.9837684,0.00000948191,0.0007541789,0.01093169,0.0004007687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5749902,0.003745475,0.3581907,0.007961356,0.03128612,0.002458691,0.00007642233,0.000749697,0.02054132],"genre_scores_gemma":[0.9932432,0.000180229,0.0001415679,0.004542129,0.0007663716,0.00008213399,0.00002082204,0.00002283794,0.001000719],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.418253,"threshold_uncertainty_score":0.9998914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04485879933440324,"score_gpt":0.2559926914064793,"score_spread":0.2111338920720761,"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."}}