{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009204188,0.0006949003,0.0007324723,0.0006602798,0.0006358284,0.001149174,0.002064478,0.0009612289,0.001243663],"category_scores_gemma":[0.00265351,0.0004997265,0.0006367265,0.000556966,0.001241518,0.001921369,0.001838714,0.001173368,0.0002620064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009919518,"about_ca_system_score_gemma":0.00134244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009170949,"about_ca_topic_score_gemma":0.008361778,"domain_scores_codex":[0.9994689,0.0001380971,0.00002558348,0.0001476368,0.0001371117,0.00008271186],"domain_scores_gemma":[0.9993359,0.000338369,0.00008172209,0.00006997817,0.0001063525,0.00006760225],"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":[0.00006236153,0.00005896112,0.001020498,0.00004581809,0.00002967331,0.0001076805,0.0002876635,0.8661676,0.005789986,0.04463396,0.0009932112,0.08080257],"study_design_scores_gemma":[0.000002935204,0.00001098373,0.0000465683,0.000001628588,0.000002501169,0.000006776659,0.00001110086,0.9958752,0.0002939897,0.00348852,0.0002559352,0.000003798513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006331536,0.00006872964,0.9924756,0.00008503027,0.000008300176,0.00002080776,0.0000119538,0.0002225461,0.0007755689],"genre_scores_gemma":[0.5721316,0.0002135044,0.4252374,0.0001583013,0.00004266245,0.0001695795,0.00007982018,0.0001201449,0.00184704],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009170949,"threshold_uncertainty_score":0.01823515,"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."}}