{"id":"W2949388523","doi":"10.48550/arxiv.1301.1027","title":"On online energy harvesting in multiple access communication systems","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Mathematical optimization; Throughput; Energy harvesting; Iterative method; Energy (signal processing); Function (biology); Transmission (telecommunications); Maximum power principle; Power (physics); Algorithm; Mathematics; Wireless; Telecommunications","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":[0.002138506,0.00116893,0.001311105,0.0009195454,0.000804174,0.001813998,0.001224755,0.001634001,0.003950978],"category_scores_gemma":[0.009792971,0.0003862764,0.0006617492,0.001318574,0.002037396,0.002725316,0.001785412,0.001642647,0.0004505977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001450533,"about_ca_system_score_gemma":0.0007857546,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003160133,"about_ca_topic_score_gemma":0.002117715,"domain_scores_codex":[0.9987049,0.0006570326,0.00004074524,0.0001557093,0.0003033121,0.000138303],"domain_scores_gemma":[0.9922171,0.0066627,0.0003726032,0.0002510834,0.0003985796,0.00009794477],"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":[0.00005834421,0.00006582992,0.0006682728,0.0001333505,0.00003375796,0.000213845,0.0001346155,0.8398212,0.001171875,0.1423638,0.001227032,0.01410802],"study_design_scores_gemma":[0.000004196854,0.00001698376,0.00007497089,0.000008720341,0.000003382474,0.00002120295,0.00001697348,0.9707389,0.000143962,0.02860696,0.0003585095,0.000005299241],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0357992,0.002934087,0.939299,0.001557084,0.0001623916,0.00007933781,0.0001078714,0.0001301206,0.01993096],"genre_scores_gemma":[0.9229742,0.004189905,0.06129542,0.0003884825,0.0003777292,0.0002315043,0.0001088015,0.0001058437,0.01032815],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003950978,"threshold_uncertainty_score":0.01321733,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0872171307447602,"score_gpt":0.1886255991000244,"score_spread":0.1014084683552642,"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."}}