{"id":"W2091633261","doi":"10.1109/tvt.2013.2283505","title":"Recursive Waterfilling for Wireless Links With Energy Harvesting Transmitters","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Recursion (computer science); Mathematical optimization; Transmission (telecommunications); Computation; Fading; Energy (signal processing); Power (physics); Constraint (computer-aided design); Algorithm; Computer science; Channel (broadcasting); Throughput; Wireless; Mathematics; Decoding methods; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007659875,0.0003797029,0.000358759,0.0004391533,0.0002404872,0.0000594712,0.0003275855,0.000786374,0.0000282479],"category_scores_gemma":[0.000003695812,0.0003539239,0.0001168133,0.0005709897,0.0001900038,0.0002165589,0.000001174419,0.0008606925,0.00001917687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001218145,"about_ca_system_score_gemma":0.0000222794,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000077871,"about_ca_topic_score_gemma":0.0001351406,"domain_scores_codex":[0.9983765,0.00002626821,0.0003467542,0.0004385777,0.0001568593,0.0006549941],"domain_scores_gemma":[0.9990957,0.0001553757,0.0000561145,0.0004437731,0.0001440533,0.0001050241],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001448402,0.00004337735,0.00001231854,0.00006137948,0.0001798079,0.00001326005,0.00007327831,0.8628159,0.02150279,0.0006406929,0.0000867961,0.114556],"study_design_scores_gemma":[0.0009887053,0.0003537091,0.00001110615,0.0003561587,0.0001043326,0.00009370852,0.0001466171,0.482646,0.5104043,0.0006941611,0.003484923,0.000716267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2271115,0.00009068502,0.7698713,0.0005377785,0.0003514196,0.0002856851,0.000008038836,0.001616286,0.0001272799],"genre_scores_gemma":[0.9772016,0.00009390926,0.02091651,0.0001567636,0.00006502819,0.001110465,0.000009367594,0.0001628305,0.0002835096],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7500901,"threshold_uncertainty_score":0.9998913,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005781512913910586,"score_gpt":0.1762528030735445,"score_spread":0.1704712901596339,"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."}}