{"id":"W2172100059","doi":"10.1109/tmc.2013.147","title":"A Scalable Bandwidth-Efficient Hybrid Adaptive Service Discovery Protocol for Vehicular Networks with Infrastructure Support","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Mobile Ad Hoc Networks","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Computer network; Service discovery; Scalability; Correctness; Service provider; Bandwidth (computing); Routing protocol; Network packet; Vehicular ad hoc network; Service (business); Distributed computing; Wireless ad hoc network; Wireless; Web service; Telecommunications; World Wide Web; Database","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":[0.00165487,0.0005595392,0.0007985008,0.001353757,0.001118057,0.001059371,0.001990926,0.0007847994,0.0008298358],"category_scores_gemma":[0.003815263,0.0003366322,0.000551262,0.001429408,0.0008854408,0.001541188,0.002170754,0.001068285,0.0002639173],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001177274,"about_ca_system_score_gemma":0.001924003,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003947566,"about_ca_topic_score_gemma":0.003980618,"domain_scores_codex":[0.9987086,0.0003031618,0.0001498387,0.0001264082,0.0005792168,0.0001327255],"domain_scores_gemma":[0.998691,0.0005274627,0.0001406391,0.0001502155,0.0004171574,0.00007358297],"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":[0.0008252289,0.000246902,0.001853263,0.0007840444,0.0003043319,0.001888347,0.001040018,0.2709332,0.07934679,0.2071885,0.01831858,0.4172707],"study_design_scores_gemma":[0.0001413139,0.000308117,0.0004576285,0.00004975127,0.0001174755,0.0009213357,0.0001603126,0.9188772,0.0135687,0.02805184,0.03722876,0.0001175238],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01816294,0.001295678,0.9734175,0.0006035604,0.0002586796,0.0005259987,0.000137955,0.00165746,0.003940277],"genre_scores_gemma":[0.7153715,0.001408861,0.2763724,0.0003588111,0.0001269952,0.001149084,0.0007729412,0.00009448794,0.004344759],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003947566,"threshold_uncertainty_score":0.008751869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00934413978443698,"score_gpt":0.2376346744005716,"score_spread":0.2282905346161347,"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."}}