{"id":"W2809742103","doi":"10.1155/2018/6387063","title":"Microscopic Congestion Detection Protocol in VANETs","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Computer science; Header; Intelligent transportation system; Network packet; Throughput; Vehicular ad hoc network; Traffic congestion; Network congestion; Computer network; Protocol (science); Traffic flow (computer networking); Real-time computing; Wireless ad hoc network; Simulation; Transport engineering; Engineering; Wireless; Telecommunications","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001036264,0.0005150724,0.0005851411,0.0009988378,0.0005732089,0.0008762229,0.001133782,0.0003848306,0.0005945462],"category_scores_gemma":[0.002802856,0.0002971165,0.0002791375,0.0007850874,0.0006699274,0.001441165,0.001467408,0.000597981,0.00009912701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007470214,"about_ca_system_score_gemma":0.0008586748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003427617,"about_ca_topic_score_gemma":0.002467338,"domain_scores_codex":[0.9993076,0.0002041001,0.00005345266,0.0001221044,0.0002342236,0.00007852036],"domain_scores_gemma":[0.999292,0.0002534612,0.0001174125,0.00009053559,0.0002041804,0.00004236654],"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":[0.0002251802,0.00009075961,0.002231555,0.0002592033,0.000128557,0.0002901071,0.0002407466,0.8004745,0.02093821,0.06548143,0.003803613,0.1058362],"study_design_scores_gemma":[0.00002406987,0.0001569099,0.0004255015,0.00001432774,0.00003396416,0.0001023136,0.00007434249,0.9779148,0.00392921,0.01237462,0.00491974,0.00003019631],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05366759,0.0007959217,0.9382773,0.0003252332,0.0002529901,0.0003071728,0.0001427031,0.001275168,0.004955927],"genre_scores_gemma":[0.937245,0.000557357,0.05916891,0.0001164332,0.00005175188,0.0002829983,0.0002017528,0.00004276822,0.002333077],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003427617,"threshold_uncertainty_score":0.006815374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005057704245801428,"score_gpt":0.2411369565170936,"score_spread":0.2360792522712922,"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."}}