{"id":"W2909444676","doi":"10.1109/tmc.2019.2893917","title":"Cooperative Caching for Multiple Bitrate Videos in Small Cell Edges","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Computer science; Server; Quality of experience; Function (biology); Mobile device; Enhanced Data Rates for GSM Evolution; Constant bitrate; Mobile edge computing; Software deployment; Computer network; Variable bitrate; Bit rate; Quality of service; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008791969,0.000706782,0.001087297,0.0004865952,0.00075792,0.001095501,0.00147263,0.001115171,0.001155618],"category_scores_gemma":[0.003300842,0.0002913089,0.000542031,0.001020547,0.0005227303,0.001236758,0.001029437,0.0005891563,0.0001844348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00115815,"about_ca_system_score_gemma":0.0008220678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007526396,"about_ca_topic_score_gemma":0.009684868,"domain_scores_codex":[0.9994451,0.000164057,0.00002366151,0.0001027513,0.00007648554,0.0001878445],"domain_scores_gemma":[0.9979913,0.001345837,0.0001767588,0.0001356023,0.0002203979,0.0001300653],"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":[0.0007228993,0.0002275334,0.002706036,0.0002259286,0.00007167595,0.0007909074,0.0002549945,0.9041959,0.01512883,0.01936716,0.003249457,0.05305872],"study_design_scores_gemma":[0.00001658374,0.00005229562,0.0001980526,0.00000473706,0.00001091726,0.00006499884,0.00005344574,0.9950835,0.001145525,0.003146265,0.0002183317,0.000005496705],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2729119,0.0009330421,0.7206033,0.0005277873,0.00005378171,0.0001863951,0.0002047798,0.0003885865,0.004190387],"genre_scores_gemma":[0.9488978,0.0002938165,0.0494221,0.00006791717,0.00002056045,0.00005583881,0.00007575755,0.00001861023,0.001147629],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007526396,"threshold_uncertainty_score":0.01496518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01972211629671811,"score_gpt":0.2312674634181534,"score_spread":0.2115453471214352,"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."}}