{"id":"W4312807099","doi":"10.1007/978-3-031-06947-5_15","title":"Semi-supervised Learning with Self-training Classifier for Cache Placement in Mobile Edge Networks","year":2022,"lang":"en","type":"book-chapter","venue":"Signals and communication technology","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Cache; Classifier (UML); Machine learning; Artificial intelligence; Training set; Enhanced Data Rates for GSM Evolution; Popularity; Data mining; Computer network","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":[0.001615975,0.0009120157,0.001731559,0.0006451443,0.0005900267,0.0008548753,0.00237349,0.001638193,0.001259194],"category_scores_gemma":[0.003491431,0.0005201557,0.0007347801,0.001000598,0.0005808533,0.001562555,0.0008828007,0.001609076,0.0009157495],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009356746,"about_ca_system_score_gemma":0.001072014,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007483761,"about_ca_topic_score_gemma":0.007800278,"domain_scores_codex":[0.9990506,0.0002901382,0.00006486762,0.0002880468,0.0001543327,0.0001520002],"domain_scores_gemma":[0.996997,0.001669656,0.0001658407,0.0002825006,0.0008063788,0.00007866567],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003111393,0.0003125758,0.002045329,0.0001305398,0.0001024365,0.0001077729,0.0001034591,0.4199797,0.004439603,0.002866393,0.01079472,0.5588064],"study_design_scores_gemma":[0.000002398081,0.00001770906,0.00008886654,0.000002653573,0.000003870046,0.00001041496,0.00000590151,0.9984982,0.0005305088,0.0006800281,0.000156825,0.000002659993],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03553367,0.0008610653,0.9598311,0.0002012302,0.0001374167,0.0000591073,0.0001874078,0.00211207,0.001076979],"genre_scores_gemma":[0.6491065,0.0004309327,0.3405643,0.0002824678,0.0002721279,0.000214337,0.001132231,0.0002505255,0.007746433],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007483761,"threshold_uncertainty_score":0.01488036,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02657591195244853,"score_gpt":0.231881744939317,"score_spread":0.2053058329868685,"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."}}