{"id":"W2771615003","doi":"10.1109/jsyst.2017.2771294","title":"Fair and Low Complexity Node Selection in Energy Harvesting Wireless Sensor Networks","year":2017,"lang":"en","type":"article","venue":"IEEE Systems Journal","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Université de Moncton; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wireless sensor network; Computer science; Energy harvesting; Key distribution in wireless sensor networks; Maximization; Sensor node; Wireless; Computer network; Distributed computing; Energy (signal processing); Wireless network; Mathematical optimization; Mathematics; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002675303,0.0007302876,0.001189359,0.0005648442,0.0008522848,0.0009240258,0.001548642,0.0008336624,0.000862088],"category_scores_gemma":[0.006117093,0.0003799452,0.0003199842,0.0008288772,0.001292662,0.001231524,0.0008894713,0.0006138986,0.0001512939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001064309,"about_ca_system_score_gemma":0.001440721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001964703,"about_ca_topic_score_gemma":0.002675004,"domain_scores_codex":[0.9986531,0.0006080457,0.0000461391,0.0002463437,0.0002578163,0.0001885221],"domain_scores_gemma":[0.9964266,0.002896142,0.0002470063,0.0001525022,0.0001759875,0.0001017831],"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":[0.0001892814,0.00005477025,0.0005346577,0.00005683344,0.00002044878,0.00007316875,0.00009724479,0.9502267,0.003186555,0.01628153,0.0004585734,0.02882025],"study_design_scores_gemma":[0.000017763,0.0000394735,0.00008820955,0.000003242151,0.000005540013,0.00002119966,0.00001415635,0.9916769,0.0006830856,0.007274086,0.0001711355,0.000005169044],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03726929,0.0003692743,0.960839,0.0001540135,0.00003715721,0.00007244261,0.00002681243,0.0001363156,0.001095745],"genre_scores_gemma":[0.8914972,0.0002817471,0.1063782,0.00008422496,0.00004439512,0.0001344026,0.00003753802,0.00002896471,0.00151323],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002675303,"threshold_uncertainty_score":0.01414853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02201029367751004,"score_gpt":0.2285362381533289,"score_spread":0.2065259444758188,"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."}}