{"id":"W2114753940","doi":"10.1109/iwcmc.2014.6906433","title":"CO-TORA on-demand routing protocol for cognitive radio ad-hoc networks","year":2014,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer network; Computer science; Cognitive radio; Routing protocol; Wireless Routing Protocol; Wireless ad hoc network; Optimized Link State Routing Protocol; Throughput; Distributed computing; Zone Routing Protocol; Cognitive network; Dynamic Source Routing; Routing (electronic design automation); Wireless; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006085279,0.0004203039,0.0004497319,0.0005191297,0.0009432507,0.001120336,0.001406715,0.00063403,0.001711405],"category_scores_gemma":[0.001616219,0.0001643089,0.0004359662,0.0006866101,0.0005074186,0.0008315741,0.0009561914,0.001074305,0.0005838642],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005975794,"about_ca_system_score_gemma":0.001332166,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002030101,"about_ca_topic_score_gemma":0.003018205,"domain_scores_codex":[0.9994418,0.0001820956,0.00004471749,0.00006729762,0.0001952405,0.00006881493],"domain_scores_gemma":[0.9991333,0.0002298025,0.00009119396,0.0001662379,0.0003183449,0.00006113679],"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":[0.001081157,0.0004230862,0.001474658,0.001363002,0.0003278621,0.001498896,0.0007271123,0.1477678,0.09715406,0.2007003,0.06713733,0.4803447],"study_design_scores_gemma":[0.0001535363,0.0005076139,0.0008954903,0.0000912713,0.0001930272,0.001408955,0.0002271449,0.8148337,0.02981192,0.04221542,0.1095145,0.0001473731],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02549737,0.002357889,0.9455695,0.0009428422,0.0007565928,0.0007361078,0.0003581978,0.002847479,0.02093409],"genre_scores_gemma":[0.6690925,0.002312474,0.3079667,0.0007063099,0.0002108357,0.001392603,0.001498896,0.0003140148,0.01650561],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002030101,"threshold_uncertainty_score":0.005725205,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02206018319667963,"score_gpt":0.2945925972216266,"score_spread":0.2725324140249469,"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."}}