{"id":"W4392796562","doi":"10.1109/tmc.2024.3377226","title":"A Unified Framework for Guiding Generative AI With Wireless Perception in Resource Constrained Mobile Edge Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Opportunistic and Delay-Tolerant Networks","field":"Computer Science","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Guangdong Provincial Pearl River Talents Program; Info-communications Media Development Authority; National Natural Science Foundation of China; Ministry of Education - Singapore; National Research Foundation Singapore","keywords":"Computer science; Wireless; Enhanced Data Rates for GSM Evolution; Wireless network; Resource (disambiguation); Generative grammar; Mobile computing; Perception; Computer network; Mobile telephony; Distributed computing; Mobile radio; Human–computer interaction; Telecommunications; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000563504,0.0003441414,0.0003790908,0.0002849208,0.00046736,0.0004430433,0.0004219089,0.0002513293,0.00002131063],"category_scores_gemma":[0.000001482843,0.0003187277,0.000154668,0.0009795624,0.0001260359,0.0002895822,0.000008973611,0.0006737534,0.00000837035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001797739,"about_ca_system_score_gemma":0.0001876204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001284426,"about_ca_topic_score_gemma":0.00001331473,"domain_scores_codex":[0.9976226,0.0001502142,0.0005199136,0.0008554462,0.0002592214,0.0005925741],"domain_scores_gemma":[0.9981558,0.001051214,0.00009178296,0.0004533336,0.0000926137,0.0001553079],"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":[0.00005029851,0.00009531371,0.000004329886,0.00003958826,0.000041149,0.00004437537,0.001603354,0.6207801,0.0001086057,0.003653825,0.0001800422,0.373399],"study_design_scores_gemma":[0.0004795554,0.0005020621,0.000004519572,0.0007560809,0.00002986938,0.00005939085,0.0005575028,0.9955716,0.0001925495,0.0006021812,0.0008638689,0.0003808253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01566495,0.0001658066,0.9809897,0.0002166281,0.001082825,0.001134764,0.00001280448,0.0005228237,0.0002096819],"genre_scores_gemma":[0.9287472,0.00002766508,0.06990092,0.0005443621,0.0003182871,0.000301995,0.0000102612,0.00004522708,0.0001040793],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9130822,"threshold_uncertainty_score":0.9999265,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02564544004550459,"score_gpt":0.2788105216645401,"score_spread":0.2531650816190355,"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."}}