{"id":"W2964907595","doi":"10.1177/1550147719866389","title":"Social-aware routing for cognitive radio–based vehicular ad hoc networks","year":2019,"lang":"en","type":"article","venue":"International Journal of Distributed Sensor Networks","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Natural Science Foundation of China","keywords":"Computer science; Computer network; Vehicular ad hoc network; Cognitive radio; Wireless ad hoc network; Optimized Link State Routing Protocol; Destination-Sequenced Distance Vector routing; Ad hoc wireless distribution service; Distributed computing; Network packet; Routing protocol; Link-state routing protocol; 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":[0.00079777,0.0006389879,0.0005537033,0.0008957774,0.001158245,0.001043923,0.001382911,0.0006466595,0.0005458195],"category_scores_gemma":[0.002465938,0.0002291381,0.0006147329,0.0008725696,0.0006032915,0.001224322,0.001092542,0.0004747395,0.0001372813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001138021,"about_ca_system_score_gemma":0.001386786,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004356287,"about_ca_topic_score_gemma":0.005384621,"domain_scores_codex":[0.9991444,0.0003214763,0.0000449741,0.0001302824,0.0002549211,0.0001038835],"domain_scores_gemma":[0.9991263,0.0003525006,0.0001547614,0.0000845582,0.0002172806,0.00006467419],"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":[0.0001032795,0.0001153981,0.001707525,0.0002826891,0.0001577037,0.000417791,0.0003867324,0.7404703,0.009270808,0.09361903,0.006040459,0.1474283],"study_design_scores_gemma":[0.00001079285,0.00004082246,0.0003082177,0.00001138553,0.00003375649,0.00008166948,0.00008626831,0.9728358,0.0008127167,0.02179183,0.003966293,0.00002055689],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03097845,0.001400051,0.9618974,0.000509773,0.0002103194,0.0001310831,0.00005927343,0.0002976721,0.004515979],"genre_scores_gemma":[0.8976895,0.001364544,0.09789212,0.000167556,0.0001628247,0.000212226,0.0001831094,0.00003826099,0.002289734],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004356287,"threshold_uncertainty_score":0.008661866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008897488924666682,"score_gpt":0.2420549562269271,"score_spread":0.2331574673022604,"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."}}