{"id":"W2100409346","doi":"10.1145/2656346.2656412","title":"A prediction based clustering algorithm for target tracking in vehicular ad-hoc networks","year":2014,"lang":"en","type":"article","venue":"","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Vehicular ad hoc network; Cluster analysis; Intelligent transportation system; Wireless ad hoc network; Tracking (education); Overhead (engineering); Video tracking; Wireless sensor network; Vehicle tracking system; Tracking system; Real-time computing; Object (grammar); Wireless; Artificial intelligence; Computer network; Kalman filter; Engineering; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006818833,0.0007212624,0.001072186,0.0009952134,0.001188547,0.0005305123,0.001965379,0.0007932867,0.0008547287],"category_scores_gemma":[0.001982471,0.000367035,0.0004749511,0.001665501,0.0003804846,0.001236284,0.0008459468,0.0009565294,0.0006701784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008482065,"about_ca_system_score_gemma":0.00115811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0170352,"about_ca_topic_score_gemma":0.01442001,"domain_scores_codex":[0.9993516,0.0001222485,0.0000394456,0.0001602682,0.0002627367,0.00006380508],"domain_scores_gemma":[0.999357,0.0001976422,0.00004961534,0.00007844011,0.0002867251,0.00003055858],"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":[0.0001699994,0.0000812727,0.001123872,0.0001009981,0.00007260227,0.00007713067,0.0001133882,0.5714194,0.005837769,0.008275466,0.006977024,0.4057511],"study_design_scores_gemma":[0.0000109185,0.00004464427,0.0002568657,0.000005829659,0.00001006869,0.00004801087,0.00001812443,0.9942367,0.001370857,0.002347186,0.001636038,0.00001464274],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008239742,0.0005780438,0.9888741,0.0001137955,0.0001083913,0.00008368614,0.00006590009,0.0008029255,0.001133433],"genre_scores_gemma":[0.3548135,0.001267279,0.6371763,0.0001758635,0.0001481952,0.0002697555,0.0008051823,0.0001509466,0.005192987],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0170352,"threshold_uncertainty_score":0.03387207,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009142133071558218,"score_gpt":0.2047512760748582,"score_spread":0.1956091430033,"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."}}