{"id":"W2891423572","doi":"10.1587/transcom.2018nvi0002","title":"Technology and Standards Accelerating 5G Commercialization","year":2018,"lang":"en","type":"article","venue":"IEICE Transactions on Communications","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"GLS Industries (Canada)","funders":"","keywords":"Computer science; Standardization; Radio access network; Enabling; Core network; Flexibility (engineering); Slicing; Cellular network; Commercialization; Telecommunications; Low latency (capital markets); Computer network; World Wide Web; Operating system; Mobile station; Base station","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":[0.0001826051,0.0001574214,0.000153934,0.0004936513,0.0008630248,0.00005351201,0.0008330698,0.0001614987,0.00005332121],"category_scores_gemma":[0.0000679825,0.000180867,0.0000276528,0.001052822,0.0006748199,0.0002578876,0.00003055261,0.0004590253,0.00002450904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001774542,"about_ca_system_score_gemma":0.00003295059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006802549,"about_ca_topic_score_gemma":0.0002952788,"domain_scores_codex":[0.9991635,0.00004869241,0.0002758879,0.0001497271,0.0001741617,0.0001880478],"domain_scores_gemma":[0.9974315,0.0001763134,0.00004828223,0.001937218,0.0003663764,0.00004035901],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001314437,0.0001762,0.0001354542,0.0000368577,0.0001053336,3.487305e-7,0.001086936,0.009887102,0.00821157,0.09892703,0.0005615617,0.8808585],"study_design_scores_gemma":[0.001605532,0.0004761294,0.0007980441,0.0003530813,0.0001035795,0.0000522147,0.00499981,0.2991326,0.1399969,0.02384233,0.5271969,0.001442873],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005927214,0.00102296,0.9683872,0.003868061,0.0001186224,0.0002711127,0.00005735506,0.002761406,0.01758605],"genre_scores_gemma":[0.957379,0.00298356,0.03928424,0.00008685718,0.00001250024,0.0001562494,0.000009819101,0.00003861269,0.00004917662],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9514518,"threshold_uncertainty_score":0.7375541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03036525387290621,"score_gpt":0.3018710113353233,"score_spread":0.2715057574624171,"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."}}