{"id":"W3014297757","doi":"10.1109/tmc.2020.2984261","title":"LeaD: Large-Scale Edge Cache Deployment Based on Spatio-Temporal WiFi Traffic Statistics","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":144,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Key Research and Development Program of China; Higher Education Discipline Innovation Project; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Cache; Computer network; Software deployment; Backhaul (telecommunications); Bottleneck; Enhanced Data Rates for GSM Evolution; Telecommunications; Operating system; Embedded system; Base station","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":[0.0007634134,0.001197968,0.0007717233,0.001256255,0.0007696834,0.0009957474,0.001694724,0.0005868714,0.0004969891],"category_scores_gemma":[0.00427774,0.0003725196,0.0004104459,0.001698278,0.0004385196,0.00171645,0.001353242,0.0005818459,0.0002983462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008301087,"about_ca_system_score_gemma":0.001217534,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01507948,"about_ca_topic_score_gemma":0.03203734,"domain_scores_codex":[0.9993873,0.0001096323,0.0000401521,0.0001836241,0.0001477483,0.0001315407],"domain_scores_gemma":[0.9984083,0.0004162048,0.0001940449,0.0003438779,0.0003901808,0.0002475115],"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":[0.0008310114,0.000686826,0.219036,0.0004791275,0.0003563719,0.001518912,0.0008292436,0.5639306,0.04007931,0.004947465,0.01646561,0.1508395],"study_design_scores_gemma":[0.0000294576,0.0001500568,0.01537201,0.00001329252,0.00004731493,0.0002872355,0.0002662779,0.9765393,0.004645524,0.001249378,0.00136419,0.00003595441],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7799349,0.0007385496,0.2027857,0.0005215365,0.0001211929,0.0004001069,0.002864643,0.009113332,0.003520033],"genre_scores_gemma":[0.9620337,0.000166764,0.03522927,0.0000608595,0.00002116157,0.00009699081,0.001860916,0.00007817765,0.0004520814],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01507948,"threshold_uncertainty_score":0.0299834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02373602989164232,"score_gpt":0.2440994077803274,"score_spread":0.2203633778886851,"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."}}