{"id":"W7140530453","doi":"10.1109/sisimpact67725.2025.11439782","title":"Energy-Efficient Resource Allocation in 6G Wireless Networks Using AI-Driven Optimization and Edge Intelligent","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Resource allocation; Wireless; Enhanced Data Rates for GSM Evolution; Wireless network; Power (physics); Resource (disambiguation); Resource management (computing); Key (lock)","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":[0.0002276241,0.0003823328,0.0004049093,0.000763181,0.0002072948,0.0001213602,0.000541328,0.0004594021,0.00002341525],"category_scores_gemma":[0.00005739644,0.0004554132,0.00005297752,0.001665806,0.0002214932,0.0001997014,0.0006883523,0.0005684226,0.000001164217],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006952135,"about_ca_system_score_gemma":0.0000594746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001078436,"about_ca_topic_score_gemma":0.00007865245,"domain_scores_codex":[0.9979075,0.0001281483,0.0008403382,0.0005032881,0.0001601304,0.0004606495],"domain_scores_gemma":[0.9984833,0.000219917,0.0001466875,0.0009567222,0.0001311953,0.00006219597],"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":[0.00001427715,0.00008055714,0.0002446084,0.00007615192,0.000034524,0.000001074068,0.0001776088,0.8884691,0.0003989461,0.02726091,0.00003835103,0.0832039],"study_design_scores_gemma":[0.0003356797,0.00001780325,0.00006307611,0.0005818595,0.00002350978,0.000001913635,0.001029053,0.9882287,0.008051485,0.0001676393,0.001126912,0.0003723613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01411424,0.007090475,0.9760365,0.0006060501,0.0002228785,0.0003842181,0.000001654814,0.0004961429,0.001047847],"genre_scores_gemma":[0.9608924,0.008133925,0.03051849,0.0001473999,0.00002229556,0.00005263658,0.00002830582,0.00005134934,0.0001531761],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9467782,"threshold_uncertainty_score":0.9997898,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01323332821938684,"score_gpt":0.2510721568145526,"score_spread":0.2378388285951658,"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."}}