{"id":"W1689783228","doi":"10.1002/wcm.1232","title":"Performance of simple cognitive personal area networks with finite buffers and adaptive superframe duration","year":2011,"lang":"en","type":"article","venue":"Wireless Communications and Mobile Computing","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Superframe; Computer science; Piconet; Network packet; Duration (music); Computer network; Duty cycle; Personal area network; Simple (philosophy); Real-time computing; Bandwidth (computing); Node (physics); Transmission (telecommunications); Network performance; Channel (broadcasting); Wireless; Telecommunications; Bluetooth; Voltage; Electrical engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002266819,0.0001558416,0.0002223075,0.00007858967,0.00048103,0.00006411467,0.0002940565,0.00005341177,0.000002426159],"category_scores_gemma":[0.000008412413,0.0001412742,0.00002769458,0.0002802356,0.0003530238,0.000279274,0.0004352315,0.0002305462,3.031097e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001624511,"about_ca_system_score_gemma":0.00003523229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000813423,"about_ca_topic_score_gemma":0.00004832458,"domain_scores_codex":[0.9990368,0.0001087247,0.000251456,0.0002761758,0.0001056792,0.0002210881],"domain_scores_gemma":[0.9985464,0.000625611,0.0001739252,0.0003765473,0.0002023463,0.00007516835],"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":[0.00008752944,0.0001517654,0.02903131,0.00002823634,0.00009900669,0.000004173608,0.01551131,0.00107569,0.0001057094,0.003491804,0.000003546093,0.9504099],"study_design_scores_gemma":[0.0003682527,0.0004665616,0.0153294,0.0002301107,0.00002220294,0.00003676879,0.001124723,0.9820899,0.0001041147,0.00002733977,0.00002080313,0.0001798033],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7067924,0.0008036393,0.2915395,0.00001836801,0.00001628882,0.0001973149,0.000002565424,0.00003983966,0.0005901107],"genre_scores_gemma":[0.9810859,0.001056828,0.01775124,0.00004733345,0.00001917974,0.00001312915,0.00001254355,0.00001090436,0.000002951854],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9810143,"threshold_uncertainty_score":0.5760993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02771401659211796,"score_gpt":0.2284523776219335,"score_spread":0.2007383610298156,"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."}}