{"id":"W4392152857","doi":"10.1109/globecom54140.2023.10436966","title":"A Graph-Based Spatial-Temporal Deep Reinforcement Learning Model for Edge Caching","year":2023,"lang":"en","type":"article","venue":"","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Reinforcement learning; Computer science; Enhanced Data Rates for GSM Evolution; Artificial intelligence; Graph; Theoretical computer science","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":[],"consensus_categories":[],"category_scores_codex":[0.0004584927,0.0001334497,0.0001392683,0.0002166998,0.0003087741,0.0001600476,0.0004412802,0.00004325536,0.00000513756],"category_scores_gemma":[0.00004962536,0.0001229488,0.0001592458,0.0002921951,0.00001398263,0.0002292248,0.0001515894,0.0001367172,0.00004237004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003373901,"about_ca_system_score_gemma":0.00007010315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007127126,"about_ca_topic_score_gemma":0.0001944252,"domain_scores_codex":[0.9988229,0.00003344848,0.0002160111,0.0003349121,0.0002420681,0.0003506997],"domain_scores_gemma":[0.9993585,0.0001104794,0.00006662835,0.0003140986,0.00006387936,0.00008641641],"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":[0.00001119742,0.00001063994,0.0003242908,0.00001599163,0.00001077695,0.000004284019,0.00035128,0.9835196,0.0004450706,0.004787854,0.0007237613,0.009795304],"study_design_scores_gemma":[0.000494767,0.00008321858,0.00004244501,0.00001644124,0.00000550413,7.851042e-7,0.00003681418,0.997848,0.000183605,0.0008423909,0.0002710846,0.0001749801],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01189183,0.00001499841,0.9854145,0.0005986072,0.0002265716,0.0002128743,4.934739e-7,0.0008032836,0.000836846],"genre_scores_gemma":[0.9889414,0.000002556696,0.006935745,0.000534474,0.00004419881,0.00005945984,0.00002690123,0.00001277384,0.003442445],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9784787,"threshold_uncertainty_score":0.5013708,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03593950672580272,"score_gpt":0.2518820076292137,"score_spread":0.215942500903411,"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."}}