{"id":"W3003399302","doi":"10.48550/arxiv.2001.10888","title":"Cross-Layer Scheduling and Beamforming in Smart-Grid Powered Cellular Networks With Heterogeneous Energy Coordination","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Computer science; Beamforming; Lyapunov optimization; Scheduling (production processes); Smart grid; Mathematical optimization; Grid; Renewable energy; Optimization problem; Distributed computing; Schedule; Real-time computing; Engineering; Algorithm; Telecommunications; Electrical engineering; Mathematics; Lyapunov equation","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":[0.0006809125,0.0007977228,0.0005678921,0.000262512,0.0003493851,0.0007765552,0.0004335757,0.0005656381,0.001300358],"category_scores_gemma":[0.001873913,0.0003021617,0.0002680419,0.0007410679,0.0007039373,0.0007719226,0.0007035955,0.0006069301,0.0002537368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008603308,"about_ca_system_score_gemma":0.0009225098,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004226507,"about_ca_topic_score_gemma":0.003198257,"domain_scores_codex":[0.9996217,0.0001477708,0.00001212019,0.00007789866,0.00007323312,0.00006707482],"domain_scores_gemma":[0.9993979,0.0003257922,0.0000984384,0.00004772361,0.00009094188,0.00003934587],"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":[0.00004097974,0.00001870472,0.0002857462,0.00002765589,0.0000130953,0.00003554309,0.00002294913,0.9748635,0.001587835,0.008359572,0.0004918294,0.01425261],"study_design_scores_gemma":[0.000003229653,0.00001246917,0.00005123121,0.000001147113,0.000001785219,0.000004981196,0.000005606147,0.9980691,0.0002244713,0.001506126,0.0001179574,0.000001938266],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04335376,0.0003792569,0.9521004,0.0002011721,0.00006763014,0.00003939478,0.00004596624,0.0001488324,0.003663697],"genre_scores_gemma":[0.9263355,0.0003506127,0.0707726,0.00008796132,0.00004938985,0.00007345492,0.0000592885,0.00002765106,0.002243561],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004226507,"threshold_uncertainty_score":0.008403838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02630795894105973,"score_gpt":0.1697658979514688,"score_spread":0.143457939010409,"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."}}