{"id":"W4406321664","doi":"10.1109/twc.2024.3525410","title":"Diffusion-Based Deep Reinforcement Learning for Resource Management in Connected Construction Equipment Networks: A Hierarchical Framework","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"BIM and Construction Integration","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Beijing Jiaotong University","keywords":"Reinforcement learning; Computer science; Diffusion; Resource management (computing); Artificial intelligence; Computer network; Distributed computing","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":[0.000159098,0.000214686,0.0002155158,0.0005105567,0.0005452305,0.00006469092,0.0003769209,0.0001972756,0.00004698525],"category_scores_gemma":[0.00000763345,0.0002437625,0.0001237448,0.0007737369,0.0001632018,0.00008085716,0.00000633728,0.0007716073,0.000005252914],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000331998,"about_ca_system_score_gemma":0.00003369031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001559162,"about_ca_topic_score_gemma":0.0001521228,"domain_scores_codex":[0.9986947,0.0001262577,0.0005199227,0.0002334603,0.0001474955,0.0002782095],"domain_scores_gemma":[0.9984158,0.000602877,0.00005941329,0.0007885112,0.00007134834,0.00006206089],"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":[0.00006850208,0.00009701848,0.00003315932,0.00004644704,0.00008462252,2.719052e-7,0.0001210695,0.8085754,0.0001182915,0.0361323,0.00004956279,0.1546734],"study_design_scores_gemma":[0.0009818056,0.00004555464,0.00007969455,0.0003543693,0.00006322046,0.000001413922,0.0006223579,0.9892295,0.0007192463,0.00110796,0.006580098,0.000214782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002929696,0.0001371994,0.9912527,0.0008940516,0.000486946,0.0008196894,0.000004854503,0.0004125277,0.003062343],"genre_scores_gemma":[0.9808333,0.0005191175,0.01673076,0.0002096267,0.00001632865,0.001442603,0.00006929356,0.00002834346,0.0001506059],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9779036,"threshold_uncertainty_score":0.9940345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01094853965217846,"score_gpt":0.2441993846879054,"score_spread":0.2332508450357269,"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."}}