{"id":"W2103127461","doi":"10.1109/jsac.2012.121111","title":"Spectrum-Aware Opportunistic Routing in Multi-Hop Cognitive Radio Networks","year":2012,"lang":"en","type":"article","venue":"IEEE Journal on Selected Areas in Communications","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":144,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Computer network; Cognitive radio; Geographic routing; Relay; Performance metric; Routing protocol; Exploit; Metrics; Channel (broadcasting); Dynamic Source Routing; Throughput; Distributed computing; Routing (electronic design automation); Wireless; Telecommunications; Computer security","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":[0.001314793,0.0005654427,0.0006696688,0.0005869458,0.0008489512,0.001015147,0.001291585,0.0007658639,0.0004166774],"category_scores_gemma":[0.003071752,0.0004058176,0.0004212527,0.0007010932,0.000845334,0.001304278,0.0009646803,0.0004829708,0.00007891723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008299806,"about_ca_system_score_gemma":0.0008957565,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002423019,"about_ca_topic_score_gemma":0.003318664,"domain_scores_codex":[0.9990795,0.0004209331,0.00003327732,0.0001146451,0.0002121608,0.0001396446],"domain_scores_gemma":[0.9983013,0.001149138,0.000192649,0.0001415925,0.0001445306,0.00007079326],"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":[0.0001028541,0.00007420043,0.0009840085,0.0001291114,0.00008562321,0.0004619112,0.0001709773,0.9021134,0.005676495,0.04713856,0.001075226,0.04198766],"study_design_scores_gemma":[0.00001135641,0.00003990783,0.0001525254,0.000006837728,0.00001734787,0.0001245776,0.00003366999,0.9824901,0.0006563831,0.01546998,0.0009848987,0.00001233399],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0557287,0.001722022,0.9361476,0.0003128593,0.0001507776,0.00008264214,0.00003809975,0.0002259695,0.005591308],"genre_scores_gemma":[0.9413405,0.0007818732,0.05637288,0.0001179993,0.00007596384,0.00009206732,0.00002584071,0.00002907933,0.001163803],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002423019,"threshold_uncertainty_score":0.006953359,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06346824116564526,"score_gpt":0.3125873279097428,"score_spread":0.2491190867440976,"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."}}