{"id":"W2291775261","doi":"10.1109/iscc.2015.7405541","title":"On the performance of localization prediction methods for vehicular Ad Hoc Networks","year":2015,"lang":"en","type":"article","venue":"","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"Fundação de Amparo à Pesquisa do Estado do Amazonas","keywords":"Computer science; Vehicular ad hoc network; Wireless ad hoc network; Trajectory; Position (finance); Sensor fusion; Set (abstract data type); Network topology; Mobility model; Distributed computing; Artificial intelligence; Computer network; Telecommunications; Wireless","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008218745,0.0001177223,0.0001354873,0.00003573268,0.00004389245,0.000014385,0.0001200835,0.0001075289,0.0000172646],"category_scores_gemma":[0.00005558438,0.0000832704,0.00005450508,0.0001829453,0.00002549053,0.00009015216,0.00001607906,0.000119026,0.000005298341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006246399,"about_ca_system_score_gemma":0.00001242528,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.695707e-7,"about_ca_topic_score_gemma":0.000002691544,"domain_scores_codex":[0.9992844,0.00006427799,0.0002085534,0.0001138648,0.0001302136,0.0001986624],"domain_scores_gemma":[0.999399,0.0001503155,0.00003511574,0.0002566529,0.00009737838,0.00006153203],"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":[0.00002136299,0.000008201797,0.00006166221,0.00002274832,0.00003203036,9.764195e-8,0.00005354109,0.9768286,0.00009593124,0.0007384277,0.006413237,0.01572414],"study_design_scores_gemma":[0.0002632679,0.0001602504,0.00006689376,0.00003255724,0.00002420909,0.000002052088,0.00002351447,0.9780244,0.002718568,0.0001801612,0.01841883,0.00008532018],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06506747,0.001355823,0.9316012,0.0000424404,0.0004575718,0.0004056919,0.000002348193,0.0001912605,0.0008762343],"genre_scores_gemma":[0.9824299,0.0003163285,0.01664195,0.0001250002,0.0001551359,0.00009187462,0.00003924084,0.00004643699,0.000154196],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9173624,"threshold_uncertainty_score":0.3395668,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01827343964568948,"score_gpt":0.2556516013748404,"score_spread":0.2373781617291509,"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."}}