{"id":"W4409410640","doi":"10.1038/s44287-025-00169-3","title":"Enhancing generative AI reliability via agentic AI in 6G-enabled edge computing","year":2025,"lang":"en","type":"article","venue":"Nature Reviews Electrical Engineering","topic":"Modular Robots and Swarm Intelligence","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Generative grammar; Reliability (semiconductor); Artificial intelligence; Enhanced Data Rates for GSM Evolution; Computer science; Generative model; Machine learning; Cognitive science; Psychology; Power (physics); Physics","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":[0.0004863513,0.0003953054,0.0003762722,0.0002753324,0.0004126431,0.001015931,0.001029062,0.0006530203,0.003677263],"category_scores_gemma":[0.002050156,0.0002116754,0.0002875309,0.0002517536,0.0008443354,0.00119475,0.001239711,0.001197781,0.0007497765],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003651968,"about_ca_system_score_gemma":0.0003725647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006530583,"about_ca_topic_score_gemma":0.001126332,"domain_scores_codex":[0.9997215,0.00009166559,0.000008968279,0.00004797154,0.00008740863,0.00004255206],"domain_scores_gemma":[0.9991518,0.000399568,0.00006150477,0.0001790922,0.0001516202,0.00005645395],"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":[0.0002615292,0.000234368,0.001440599,0.000317674,0.00008614305,0.0004225251,0.0005926861,0.5009442,0.06415742,0.2586115,0.005801087,0.1671304],"study_design_scores_gemma":[0.0000146194,0.0000792301,0.0003024278,0.00002266362,0.00002050553,0.0001038917,0.00007421843,0.9033163,0.009617617,0.08070598,0.005728154,0.00001435296],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1076096,0.0009251355,0.8508623,0.0008076544,0.0002438946,0.00006307231,0.00005615061,0.001353814,0.03807823],"genre_scores_gemma":[0.9221693,0.0002890381,0.07290926,0.0001776358,0.00004419572,0.00003632935,0.00004557425,0.0001402282,0.004188507],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003677263,"threshold_uncertainty_score":0.01230162,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005535461921663012,"score_gpt":0.2577097568243971,"score_spread":0.2521742949027341,"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."}}