{"id":"W3153801153","doi":"10.1145/3446679","title":"Mobility Trace Analysis for Intelligent Vehicular Networks","year":2021,"lang":"en","type":"review","venue":"ACM Computing Surveys","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Computer science; TRACE (psycholinguistics); Vehicular ad hoc network; Mobility model; Intelligent transportation system; Volume (thermodynamics); Preprocessor; Data science; Data pre-processing; Domain (mathematical analysis); Telecommunications; Data mining; Artificial intelligence; Transport engineering; Wireless ad hoc network; 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":[0.002024512,0.001093405,0.001190052,0.005989067,0.0004950898,0.002120449,0.002050814,0.001212853,0.003009538],"category_scores_gemma":[0.009791234,0.0004231058,0.001049729,0.007208126,0.0006938633,0.002933207,0.001131983,0.00146971,0.002392332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001328115,"about_ca_system_score_gemma":0.001794223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006573731,"about_ca_topic_score_gemma":0.005273144,"domain_scores_codex":[0.9985318,0.0004208396,0.000111193,0.000263868,0.0005923152,0.00007991994],"domain_scores_gemma":[0.9947495,0.00295603,0.000307527,0.0003034863,0.001588661,0.00009487113],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000332059,0.00006478222,0.002650144,0.004021386,0.0001531905,0.0001089293,0.0001753151,0.005881037,0.000584643,0.03249781,0.02423637,0.9295933],"study_design_scores_gemma":[0.00001818224,0.0001293638,0.007374534,0.007247674,0.0003640689,0.001303655,0.0009935384,0.04293419,0.002638866,0.0729182,0.8639228,0.0001549378],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00335741,0.8032917,0.1696437,0.003765281,0.001714312,0.0002789921,0.001330037,0.0006845302,0.01593408],"genre_scores_gemma":[0.0630383,0.8546077,0.06947513,0.0007348081,0.001792524,0.0003074465,0.003468587,0.00016764,0.006407873],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.006573731,"threshold_uncertainty_score":0.01307094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1093967654289085,"score_gpt":0.4037365143127341,"score_spread":0.2943397488838256,"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."}}