{"id":"W3172943015","doi":"10.1049/ntw2.12029","title":"Performance of cache placement using supervised learning techniques in mobile edge networks","year":2021,"lang":"en","type":"article","venue":"IET Networks","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Cache; Enhanced Data Rates for GSM Evolution; Cache algorithms; Computer architecture; Artificial intelligence; Computer network; CPU cache","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004809794,0.0001457487,0.0002445134,0.00007759556,0.0001004826,0.00007081101,0.0003840729,0.0001178303,0.00001259052],"category_scores_gemma":[0.00001138557,0.0001521777,0.00008130109,0.000537391,0.00003036782,0.0002487075,0.0003246014,0.0004474445,0.000001251555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007503711,"about_ca_system_score_gemma":0.00006940625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005728256,"about_ca_topic_score_gemma":0.00002001533,"domain_scores_codex":[0.9986523,0.0001538787,0.0003212713,0.0003350078,0.000183922,0.0003536361],"domain_scores_gemma":[0.9992757,0.00009039892,0.00009540016,0.0003908421,0.00009361555,0.00005399348],"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":[0.00001589687,0.00006035855,0.02119667,0.00001637671,0.00001292112,0.0000273464,0.0002312064,0.9325004,0.001256221,0.00005603669,0.0001065576,0.04451996],"study_design_scores_gemma":[0.0002210873,0.00009984187,0.0006449042,0.0002017911,0.000006549256,0.00001676568,0.00007778884,0.9968347,0.001313181,0.000004403516,0.0004134455,0.0001655707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7100523,0.00221455,0.2867088,0.00002206588,0.0003182739,0.0001391925,2.7344e-7,0.0001187442,0.00042581],"genre_scores_gemma":[0.9948573,0.0005638911,0.00413601,0.0001130168,0.0001458533,0.00002252364,0.000006904734,0.00001271713,0.0001417633],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2848051,"threshold_uncertainty_score":0.6205626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01792510850941208,"score_gpt":0.2354074568229134,"score_spread":0.2174823483135013,"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."}}