{"id":"W2805929175","doi":"10.1109/comst.2018.2841901","title":"Localization Prediction in Vehicular Ad Hoc Networks","year":2018,"lang":"en","type":"article","venue":"IEEE Communications Surveys & Tutorials","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":94,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Vehicular ad hoc network; Wireless ad hoc network; Trajectory; Position (finance); Network topology; Set (abstract data type); Sensor fusion; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009234568,0.0005736256,0.0007238914,0.0006987313,0.0005289281,0.0009128046,0.0008532705,0.0007357068,0.0007409609],"category_scores_gemma":[0.00496793,0.0003479533,0.0003329933,0.001496946,0.0004971718,0.001427085,0.00067683,0.0007947823,0.000307837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007248099,"about_ca_system_score_gemma":0.0006484295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01455059,"about_ca_topic_score_gemma":0.005929375,"domain_scores_codex":[0.9993497,0.0001969861,0.00004004474,0.0001360956,0.0002129852,0.00006428123],"domain_scores_gemma":[0.9986272,0.0008391237,0.0001523488,0.000083019,0.0002701714,0.00002823319],"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":[0.00003323823,0.0000099023,0.001740253,0.00005812506,0.00001936147,0.00007254852,0.00005193097,0.9323468,0.0004375494,0.009499466,0.001537301,0.05419354],"study_design_scores_gemma":[0.000002330109,0.0000112151,0.0002953429,0.000009228625,0.000005854664,0.0000213052,0.00002286334,0.9898186,0.0002234173,0.00858107,0.001002731,0.000005959824],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.02132585,0.002696095,0.9718571,0.000541236,0.0001683159,0.00003767075,0.0001384549,0.0006233213,0.002611971],"genre_scores_gemma":[0.9178252,0.005164773,0.0727165,0.0001300034,0.0002503435,0.0001049206,0.0004912625,0.00006264116,0.003254407],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.01455059,"threshold_uncertainty_score":0.0289318,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0256340918700639,"score_gpt":0.2589775161637297,"score_spread":0.2333434242936658,"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."}}