{"id":"W4401210868","doi":"10.1109/mnet.2024.3436670","title":"Toward Effective Retrieval Augmented Generative Services in 6G Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Network","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; University of Windsor","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Computer science; Generative grammar; Computer network; Information retrieval; Multimedia; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001695981,0.000694885,0.0005538015,0.0006829873,0.0007273704,0.002344891,0.001367893,0.001450964,0.002014166],"category_scores_gemma":[0.003910395,0.000367647,0.0004720422,0.001020286,0.001210544,0.003212358,0.004181418,0.001824124,0.001043294],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008097482,"about_ca_system_score_gemma":0.001351156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004416065,"about_ca_topic_score_gemma":0.005473558,"domain_scores_codex":[0.9987331,0.0004785522,0.0000605325,0.000138548,0.0004106208,0.0001786658],"domain_scores_gemma":[0.9989255,0.0003924588,0.00008213519,0.0002907489,0.000215306,0.0000938203],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004566488,0.0002804042,0.002384156,0.0003713254,0.00009506668,0.001346826,0.002429138,0.1755064,0.05905012,0.2914703,0.01794141,0.4486682],"study_design_scores_gemma":[0.00004070793,0.00009817578,0.0004303767,0.00004367622,0.00003330272,0.0004453195,0.0006392646,0.8563119,0.01165896,0.09806631,0.03218633,0.00004566734],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02640049,0.0007378418,0.9613463,0.001455165,0.00008692977,0.0001158236,0.00009484604,0.001788255,0.00797429],"genre_scores_gemma":[0.6058679,0.001140629,0.3863964,0.0006149526,0.000132159,0.0001170168,0.0003301917,0.000212484,0.005188414],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004416065,"threshold_uncertainty_score":0.008969307,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.010964504022135,"score_gpt":0.2437281632927417,"score_spread":0.2327636592706067,"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."}}