{"id":"W4297802385","doi":"10.1109/iccworkshops53468.2022.9882153","title":"GNN-GM: A Proactive Caching Scheme for Named Data Networking","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; University of Ottawa","funders":"","keywords":"Computer science; Cache; False sharing; Latency (audio); Smart Cache; Quality of experience; Scheme (mathematics); Computer network; Cache algorithms; Maximization; CPU cache; Quality of service","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","sts","open_science"],"consensus_categories":[],"category_scores_codex":[0.001766157,0.0004028195,0.0003870633,0.0004526485,0.002085789,0.0008500097,0.01578961,0.0001169223,0.0001900798],"category_scores_gemma":[0.0002644916,0.0004620096,0.0002203262,0.0009737044,0.0001564994,0.001075403,0.006364387,0.001769707,0.00003444379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005429697,"about_ca_system_score_gemma":0.0003797365,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001114191,"about_ca_topic_score_gemma":0.000169918,"domain_scores_codex":[0.9958291,0.0006126926,0.0007247903,0.001187379,0.001052688,0.0005933505],"domain_scores_gemma":[0.9917427,0.001412702,0.0005008315,0.005749206,0.0004288211,0.0001657243],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005479517,0.001844208,0.0005224824,0.00001975119,0.001186471,0.00003086119,0.002867208,0.0190763,0.002002157,0.5502183,0.0593257,0.3623586],"study_design_scores_gemma":[0.0007623746,0.00008640639,0.0001173142,0.0001188802,0.00003402162,0.0000351174,0.001234116,0.9382281,0.00001617634,0.003467083,0.05536944,0.0005309195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007842897,0.001916812,0.8511885,0.08671483,0.01151669,0.003056457,0.0008926705,0.001345057,0.03552606],"genre_scores_gemma":[0.9723142,0.0005090303,0.02001693,0.001876971,0.0004408791,0.001339535,0.001082765,0.00005483174,0.002364899],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9644713,"threshold_uncertainty_score":0.9997832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2400391583298681,"score_gpt":0.3697348987565371,"score_spread":0.1296957404266691,"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."}}