{"id":"W1826365044","doi":"10.1007/978-3-642-31368-4_5","title":"Vehicular Ad-hoc Networks(VANETs): Capabilities, Challenges in Information Gathering and Data Fusion","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Vehicular ad hoc network; Computer science; Wireless ad hoc network; Quality of service; Computer network; Broadcasting (networking); Intelligent transportation system; Context (archaeology); Computer security; Telecommunications; Transport engineering; Wireless; Engineering","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":[0.001124797,0.0007817221,0.0007864524,0.001083434,0.000438943,0.004144972,0.001120185,0.001404592,0.003073148],"category_scores_gemma":[0.001838639,0.0006986657,0.0004451836,0.002727799,0.001238176,0.006456797,0.001524185,0.002204786,0.002352045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000538685,"about_ca_system_score_gemma":0.0006672353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007504985,"about_ca_topic_score_gemma":0.0008047333,"domain_scores_codex":[0.9994484,0.00009949271,0.0000450231,0.00009018229,0.0002848684,0.00003202346],"domain_scores_gemma":[0.9991587,0.0005392033,0.00003534339,0.00007698679,0.000156892,0.00003284321],"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":[0.00005243133,0.0000503539,0.0004285591,0.001472553,0.00004423295,0.0001912191,0.0003351104,0.01664693,0.004870116,0.3423405,0.04136861,0.5921993],"study_design_scores_gemma":[0.000007951042,0.00009264112,0.0003881912,0.0007525348,0.00003950332,0.0008366527,0.0003330625,0.03919096,0.003952052,0.3062049,0.648136,0.000065615],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.006018064,0.3799793,0.484962,0.009903738,0.004445306,0.00009559666,0.0004484619,0.0007204328,0.1134272],"genre_scores_gemma":[0.1279656,0.5527917,0.2250301,0.001853825,0.004309019,0.0001377025,0.0009066291,0.0003330036,0.08667237],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.004144972,"threshold_uncertainty_score":0.01028073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02175473515595068,"score_gpt":0.2173737793728305,"score_spread":0.1956190442168799,"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."}}