{"id":"W3109137557","doi":"10.1109/tii.2020.3041159","title":"Intelligent-Driven Green Resource Allocation for Industrial Internet of Things in 5G Heterogeneous Networks","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":70,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Computer science; Resource allocation; Reinforcement learning; Resource management (computing); Quality of service; Distributed computing; Reliability (semiconductor); Industrial Internet; Resource (disambiguation); The Internet; Asynchronous communication; Computer network; Artificial intelligence; Internet of Things; Computer security; World Wide Web","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":[0.0004003138,0.0002223032,0.0003470751,0.0002471981,0.00009607375,0.0001209,0.0008358724,0.0003933507,0.000004360976],"category_scores_gemma":[0.00004400899,0.0002293048,0.0001604846,0.0007184698,0.00005137079,0.0006451541,0.00001474279,0.0007497028,0.00001116227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001109318,"about_ca_system_score_gemma":0.0001260848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001454981,"about_ca_topic_score_gemma":0.000005269731,"domain_scores_codex":[0.9979654,0.00007689427,0.001115234,0.0002007791,0.000285328,0.0003563453],"domain_scores_gemma":[0.9987785,0.000308079,0.000377045,0.000294731,0.00009259918,0.0001490031],"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":[0.0003656057,0.0001366062,0.00004168771,0.00005367034,0.00009944045,0.000002500311,0.01865799,0.4918129,0.00004038903,0.00014932,0.004657769,0.4839821],"study_design_scores_gemma":[0.001497183,0.0005525514,0.00000126742,0.0001237363,0.00002205288,0.000005463619,0.0001633649,0.9789314,0.01044949,0.00003612879,0.007991849,0.0002255391],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01935328,0.000005503592,0.9754118,0.0006465456,0.003616975,0.0006654053,0.000002607511,0.0001133997,0.0001844654],"genre_scores_gemma":[0.9915034,0.000006104138,0.006103763,0.0009558859,0.00130072,0.00003697936,0.00001379115,0.00002291238,0.00005642639],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9721501,"threshold_uncertainty_score":0.935078,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08396519590630679,"score_gpt":0.2576060143574497,"score_spread":0.1736408184511429,"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."}}