{"id":"W4414538454","doi":"10.1109/icc52391.2025.11161064","title":"Semantic-Aware Spectrum Efficiency for 6G V2x URLLC with Multi-Agent Hierarchical DRL","year":2025,"lang":"en","type":"article","venue":"","topic":"Power Line Communications and Noise","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"National Science and Technology Council","keywords":"Spectral efficiency; Encoder; Transmission (telecommunications); Channel (broadcasting); Embedding; Semantic similarity; Resource (disambiguation); Reinforcement learning","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":[],"consensus_categories":[],"category_scores_codex":[0.00008448969,0.0001230693,0.0001350038,0.0001030945,0.0001014672,0.00003012362,0.0002794127,0.00003655926,0.00005059523],"category_scores_gemma":[0.00001609132,0.000096993,0.00005285711,0.0002321895,0.00003454478,0.00004104031,0.00006565981,0.0001350079,0.00002226746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003860908,"about_ca_system_score_gemma":0.00003447372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001582089,"about_ca_topic_score_gemma":0.0001443039,"domain_scores_codex":[0.9993812,0.00001097574,0.0001638607,0.0001415949,0.00006531593,0.0002369815],"domain_scores_gemma":[0.9993131,0.00008845468,0.00001055448,0.0005051068,0.00002735922,0.0000554839],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006595646,0.006792864,0.03217664,0.003876815,0.002328737,0.00008781593,0.005399944,0.2067693,0.01943708,0.4647756,0.1781998,0.07949588],"study_design_scores_gemma":[0.001461025,0.0001006984,0.005249862,0.0001222383,0.0000495494,0.000004518633,0.0001253951,0.915976,0.006180264,0.0004511303,0.06993111,0.0003481634],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03952331,0.0004411354,0.9462873,0.001458418,0.0002083158,0.0004351703,0.00001491854,0.0004715319,0.01115993],"genre_scores_gemma":[0.9798287,0.00007169114,0.01659514,0.00009336421,0.00002222095,0.00006296573,0.00001494493,0.00002036707,0.003290597],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9403054,"threshold_uncertainty_score":0.395526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01233157696858064,"score_gpt":0.2545504090116935,"score_spread":0.2422188320431129,"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."}}