{"id":"W2107796859","doi":"10.1504/ijcnds.2009.026558","title":"Cognitive networking of large scale wireless systems","year":2009,"lang":"en","type":"article","venue":"International Journal of Communication Networks and Distributed Systems","topic":"Opportunistic and Delay-Tolerant Networks","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Computer network; Wireless WAN; Cognitive radio; Wireless network; Municipal wireless network; Wi-Fi array; Wireless mesh network; Wireless broadband; Wireless; Key distribution in wireless sensor networks; Radio resource management; Cognitive network; Quality of service; Multi-frequency network; Distributed computing; 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":[0.000513104,0.0003781666,0.0003579963,0.0003843914,0.000520391,0.00122975,0.0007442019,0.0005386388,0.001127725],"category_scores_gemma":[0.001796738,0.0001669165,0.0002040188,0.0004656092,0.001093291,0.001573776,0.00104917,0.0007705479,0.0001587096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005008861,"about_ca_system_score_gemma":0.0003654762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001130695,"about_ca_topic_score_gemma":0.0009213506,"domain_scores_codex":[0.9997227,0.0000741348,0.000007608385,0.00003643943,0.0001124046,0.00004671096],"domain_scores_gemma":[0.9993367,0.0004060337,0.00006461777,0.00006601382,0.0000778293,0.00004893595],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007668469,0.00005969249,0.0008619973,0.0002684978,0.00007903311,0.0006159903,0.0003380175,0.2447616,0.00925294,0.6463489,0.00532873,0.09200783],"study_design_scores_gemma":[0.00003035017,0.00006472369,0.0004534435,0.00003271461,0.00002725573,0.000276586,0.0001036497,0.6264555,0.001369185,0.3526934,0.01846388,0.00002928849],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03317521,0.005440081,0.9365032,0.001188088,0.0005391647,0.00006186213,0.00003305221,0.0003113281,0.02274789],"genre_scores_gemma":[0.9205982,0.004645718,0.06814769,0.000352571,0.0005860571,0.0001431865,0.00003358003,0.00003010749,0.005462924],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00122975,"threshold_uncertainty_score":0.003772616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01706767653259575,"score_gpt":0.2775984651340733,"score_spread":0.2605307886014775,"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."}}