{"id":"W2964334230","doi":"10.1109/mvt.2016.2645318","title":"Connected Vehicular Transportation: Data Analytics and Traffic-Dependent Networking","year":2017,"lang":"en","type":"article","venue":"IEEE Vehicular Technology Magazine","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thompson Rivers University","funders":"National Natural Science Foundation of China","keywords":"Taxis; Intelligent transportation system; Computer science; Key (lock); Analytics; Service (business); Big data; Real-time data; Broadband; Computer network; Telecommunications; Transport engineering; Engineering; Computer security; Data science; World Wide Web","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":[0.0007814215,0.0008145463,0.0005885207,0.001763911,0.0004542217,0.002588606,0.001144352,0.001004792,0.000615103],"category_scores_gemma":[0.003267525,0.0004462528,0.000346039,0.00317466,0.0008026608,0.003215449,0.001230198,0.001030022,0.0003094554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009730848,"about_ca_system_score_gemma":0.0007616223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004559252,"about_ca_topic_score_gemma":0.003257061,"domain_scores_codex":[0.9993325,0.0001510217,0.00003840487,0.0001303292,0.0003076203,0.00004012609],"domain_scores_gemma":[0.9990532,0.0004151806,0.000103981,0.000156908,0.0002175251,0.00005328709],"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":[0.0001544643,0.0001764749,0.01567277,0.000580701,0.0001729766,0.0006086471,0.0004888016,0.238981,0.006380785,0.2103396,0.02891015,0.4975336],"study_design_scores_gemma":[0.000009256117,0.0000558029,0.002749576,0.0001177999,0.00003341235,0.0004415276,0.0003186479,0.7524343,0.002871102,0.1830712,0.05785454,0.00004277618],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.040204,0.02167654,0.9100961,0.008059613,0.000781097,0.0001606563,0.001153984,0.002091793,0.01577612],"genre_scores_gemma":[0.7723331,0.03250961,0.1821956,0.0009402577,0.00154947,0.0001969817,0.003756924,0.0003071755,0.006210893],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004559252,"threshold_uncertainty_score":0.009065449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0245002124179904,"score_gpt":0.2428911612234809,"score_spread":0.2183909488054905,"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."}}