{"id":"W2034232732","doi":"10.1109/isit.2013.6620494","title":"On optimal online power policies for energy harvesting with finite-state Markov channels","year":2013,"lang":"en","type":"article","venue":"","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Fading; Markov process; Channel (broadcasting); Computer science; Ergodicity; Mathematical optimization; Markov chain; Throughput; Finite state; Ergodic theory; Channel state information; Energy harvesting; Wireless; Applied mathematics; Energy (signal processing); Mathematics; Computer network; Telecommunications; Mathematical analysis","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":[0.00007520048,0.0003155657,0.0002495327,0.0001272246,0.00008969119,0.0001448847,0.000210277,0.0000964674,0.0001337062],"category_scores_gemma":[0.00009349305,0.0002572516,0.00005470853,0.0002041228,0.00005362035,0.0002464813,0.00003876718,0.0001651224,0.00001845545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004656353,"about_ca_system_score_gemma":0.00001487594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003880192,"about_ca_topic_score_gemma":0.0001611551,"domain_scores_codex":[0.9986812,0.00001703728,0.0002712702,0.0002605331,0.0001609973,0.000608896],"domain_scores_gemma":[0.9987445,0.00066509,0.00004930978,0.000287843,0.0001033048,0.0001499418],"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":[0.00001370174,0.0000245387,0.0000318924,0.00002405788,0.00004754905,0.000006461743,0.0001015047,0.9891118,0.0001888798,0.003085202,0.003960059,0.0034044],"study_design_scores_gemma":[0.0005211206,0.0002900007,0.0002580617,0.0001771767,0.000009925251,0.00001344602,0.00006732645,0.990555,0.002326461,0.0003096051,0.004968826,0.0005029961],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5580058,0.00007843028,0.4232092,0.0002322609,0.0005239962,0.0002843257,0.00003109476,0.001463852,0.01617113],"genre_scores_gemma":[0.948804,0.00001729163,0.03779191,0.0004869777,0.0002073872,0.0001685247,0.00005624438,0.000149428,0.01231828],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3907982,"threshold_uncertainty_score":0.999988,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008657647251486853,"score_gpt":0.199264832778924,"score_spread":0.1906071855274371,"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."}}