{"id":"W2545329220","doi":"10.1109/tic-sth.2009.5444432","title":"Cognitive Wireless Sensor Networks: Emerging topics and recent challenges","year":2009,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University; Toronto Metropolitan University","funders":"","keywords":"Cognitive radio; Wireless sensor network; Computer science; Spectrum management; Computer network; Wireless; Fading; Cognitive network; Bluetooth; Physical layer; Key distribution in wireless sensor networks; Wireless network; Telecommunications; Channel (broadcasting)","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":[0.0001587424,0.0001510585,0.0001792953,0.00005701263,0.0001507283,0.0001331279,0.0001314884,0.00005727468,0.000009708179],"category_scores_gemma":[0.00001882251,0.0001356934,0.00003370333,0.0002007092,0.00003346628,0.0002213661,0.00007453325,0.0001481222,0.00000358244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001910983,"about_ca_system_score_gemma":0.00001317688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004937424,"about_ca_topic_score_gemma":0.00003106288,"domain_scores_codex":[0.9988973,0.00006066223,0.0001562215,0.0004029432,0.0001410513,0.0003418649],"domain_scores_gemma":[0.9994619,0.0001111053,0.00004721645,0.0001683368,0.0001010877,0.0001103033],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004880872,0.0000224977,0.00004242156,0.000001897404,0.0000112046,0.00003592588,0.0004185092,0.00003626359,0.00001712311,0.03069215,0.00007275977,0.9686444],"study_design_scores_gemma":[0.001034167,0.0004039805,0.02681314,0.0002205718,0.00002948288,0.0002197262,0.0009107683,0.951865,0.0006379568,0.006921788,0.0101301,0.0008133134],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04677876,0.007922057,0.8772015,0.02822641,0.0004643281,0.0002674892,4.967928e-7,0.0003922632,0.03874673],"genre_scores_gemma":[0.9785635,0.01418338,0.005081676,0.00157163,0.0004124899,0.000001123639,0.000001096376,0.000006579702,0.0001785426],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9678311,"threshold_uncertainty_score":0.5533414,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02338673678747529,"score_gpt":0.2553466623431341,"score_spread":0.2319599255556588,"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."}}