{"id":"W2143465450","doi":"10.1109/iccw.2009.5208063","title":"A Novel Localized Data Aggregation Algorithm for Advanced Vehicular Traffic Information Systems","year":2009,"lang":"en","type":"article","venue":"","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Vehicular ad hoc network; Wireless ad hoc network; Scalability; Computer network; Backhaul (telecommunications); Provisioning; Vehicular communication systems; Distributed computing; Floating car data; Algorithm; Traffic congestion; Wireless; Base station; Transport engineering; Engineering; 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":[0.0005405544,0.0006075273,0.0006822938,0.0007461079,0.0006480029,0.0008256056,0.000920318,0.0005336872,0.0009508899],"category_scores_gemma":[0.001485449,0.0002230873,0.0003488396,0.000832426,0.0002816933,0.001189686,0.0008364075,0.0007752698,0.0005244649],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005881988,"about_ca_system_score_gemma":0.0007450027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001338578,"about_ca_topic_score_gemma":0.001551619,"domain_scores_codex":[0.9996031,0.00008057637,0.00003142074,0.00009366315,0.0001536743,0.00003746381],"domain_scores_gemma":[0.9995313,0.0001389552,0.00005664671,0.0000825324,0.0001671915,0.00002350198],"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":[0.0002152642,0.0001244949,0.001008998,0.0001852592,0.00009239683,0.0001645864,0.0002245162,0.2635258,0.03451244,0.02864936,0.007316374,0.6639804],"study_design_scores_gemma":[0.00002867645,0.0001049831,0.0002129274,0.00001167496,0.00002357754,0.0001091451,0.00003210318,0.9823714,0.005611126,0.004918357,0.006559855,0.00001618695],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004769366,0.0002261837,0.9937597,0.00006744015,0.00006872099,0.00004062551,0.00001869997,0.0003933438,0.0006558393],"genre_scores_gemma":[0.2432359,0.000420376,0.7529615,0.000111966,0.0001631587,0.0002480923,0.0002187064,0.00007256457,0.002567733],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001338578,"threshold_uncertainty_score":0.004267693,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.012630380190146,"score_gpt":0.2270861664327468,"score_spread":0.2144557862426008,"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."}}