{"id":"W4404058574","doi":"10.1016/j.hcc.2024.100277","title":"Learning-based cooperative content caching and sharing for multi-layer vehicular networks","year":2024,"lang":"en","type":"article","venue":"High-Confidence Computing","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"State Key Laboratory of Industrial Control Technology; Zhejiang University; National Natural Science Foundation of China","keywords":"Computer science; Layer (electronics); Computer network; Content (measure theory); Multimedia; Materials 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008843084,0.0006548626,0.001084016,0.0004951535,0.0006611039,0.0007407742,0.002118157,0.000750393,0.000599641],"category_scores_gemma":[0.002414073,0.0003679903,0.0004545966,0.0006053099,0.0006468894,0.0009530186,0.0008522852,0.000668696,0.0001442849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001481258,"about_ca_system_score_gemma":0.001299174,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01726237,"about_ca_topic_score_gemma":0.01461956,"domain_scores_codex":[0.9995292,0.0001124471,0.00002478154,0.0001020075,0.0001054013,0.000126126],"domain_scores_gemma":[0.9988959,0.0005400067,0.0001701027,0.00008572361,0.0002252983,0.00008289496],"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":[0.0000660706,0.00005002099,0.0008899296,0.00003510975,0.00003080249,0.00006388514,0.00006195043,0.9678724,0.00174392,0.003037059,0.0005224257,0.02562637],"study_design_scores_gemma":[0.000002430673,0.00001309607,0.00004537609,9.22328e-7,0.000004026419,0.000006683897,0.000005264409,0.9990804,0.0001668369,0.0006089153,0.00006394564,0.000002081172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08752172,0.0007568294,0.9093008,0.0002086772,0.00006301509,0.00004986644,0.00003309695,0.0003639749,0.001702067],"genre_scores_gemma":[0.9790725,0.0001657896,0.01961508,0.00004911009,0.00002321417,0.00004036525,0.00004005654,0.00001399743,0.0009799766],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01726237,"threshold_uncertainty_score":0.03432381,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06331262196225164,"score_gpt":0.2856616926850933,"score_spread":0.2223490707228417,"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."}}