{"id":"W4408860680","doi":"10.1109/jiot.2025.3554775","title":"Transcoding-Enabled Edge Caching Strategy Optimization: A Dual-Timescale Meta-Learning-Based Stackelberg Game Approach","year":2025,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Stackelberg competition; Transcoding; Dual (grammatical number); Enhanced Data Rates for GSM Evolution; Game theory; Distributed computing; Computer network; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001418844,0.0003149594,0.0006220621,0.0004872907,0.0001898404,0.0007532166,0.001235519,0.0001358126,0.00008756825],"category_scores_gemma":[0.00009966741,0.0002683365,0.0006133516,0.000439094,0.00007561473,0.001133822,0.0001000296,0.001158376,0.000005398098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001426722,"about_ca_system_score_gemma":0.0003349174,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001779943,"about_ca_topic_score_gemma":0.000002153487,"domain_scores_codex":[0.9973981,0.0003886378,0.0007943033,0.0004568502,0.0005405826,0.0004215265],"domain_scores_gemma":[0.9984477,0.0002000386,0.0004852282,0.0003784727,0.0003322893,0.0001562914],"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":[0.0000826484,0.0001741716,0.0000663247,0.00007999322,0.0009327262,0.00005269423,0.0015702,0.9898854,0.001857357,0.001560869,0.00220986,0.001527711],"study_design_scores_gemma":[0.001004696,0.0002097038,0.000009593543,0.0001926913,0.0003418831,0.0001638523,0.0001358171,0.9930075,0.00381486,0.000346942,0.0005171886,0.0002552287],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01560985,0.000671196,0.9764923,0.0007761489,0.000838009,0.0001338367,0.000001359654,0.0001478304,0.00532945],"genre_scores_gemma":[0.9736363,0.00002556555,0.02022076,0.00053318,0.00008850203,0.000009433766,0.000003240482,0.00002020169,0.005462812],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9580265,"threshold_uncertainty_score":0.9999769,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02613619530213774,"score_gpt":0.2469643942487626,"score_spread":0.2208281989466249,"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."}}