{"id":"W2292424830","doi":"10.1109/glocomw.2015.7414207","title":"Self-Powered Wireless Sensor Network for Environmental Monitoring","year":2015,"lang":"en","type":"article","venue":"","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Wireless sensor network; Computer science; Environmental monitoring; Energy harvesting; Wireless; Environmental data; Real-time computing; Mobile wireless sensor network; Key distribution in wireless sensor networks; Energy (signal processing); Efficient energy use; Wireless network; Embedded system; Computer network; Environmental science; Telecommunications; Electrical engineering; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0001667739,0.0002033838,0.0001868077,0.00002569166,0.0000618985,0.00004120135,0.0001569132,0.000130761,0.00001060224],"category_scores_gemma":[0.000009523733,0.000212981,0.00005743926,0.00008306924,0.00002005476,0.0001173864,0.00003839825,0.0001366089,0.00004001108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001681308,"about_ca_system_score_gemma":0.000009897373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003185368,"about_ca_topic_score_gemma":0.00000293393,"domain_scores_codex":[0.9989392,0.00001723052,0.0002153095,0.0001972456,0.0001605601,0.0004705075],"domain_scores_gemma":[0.9994027,0.0001153228,0.00002647659,0.0002411524,0.00001339012,0.0002009527],"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":[0.00001245013,0.00002869196,0.01129778,0.00004081062,0.00009558598,0.000007666729,0.0001524211,0.973271,0.0009281741,0.0003931144,0.007315876,0.006456404],"study_design_scores_gemma":[0.001193438,0.0000828349,0.001490388,0.00007229103,0.00004402538,0.0000196192,0.0002506459,0.9091861,0.007504401,0.0001668575,0.07925778,0.0007316357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8569254,0.001792314,0.09417845,0.00004721637,0.007001849,0.0006684334,0.00001874031,0.004910975,0.03445656],"genre_scores_gemma":[0.9015529,0.00009145826,0.09503744,0.00001736644,0.001924362,0.00007127217,0.00001795247,0.0001071049,0.001180216],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0719419,"threshold_uncertainty_score":0.8685113,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01626113664267309,"score_gpt":0.2084365668974192,"score_spread":0.1921754302547461,"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."}}