{"id":"W2342878105","doi":"10.1109/comst.2016.2521642","title":"In-Vehicle Networks Outlook: Achievements and Challenges","year":2016,"lang":"en","type":"article","venue":"IEEE Communications Surveys & Tutorials","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":163,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"AUTO21 Network of Centres of Excellence; Natural Sciences and Engineering Research Council of Canada; Ontario Centres of Excellence; CMC Microsystems","keywords":"Automotive industry; Quality of service; Computer science; Service (business); Quality (philosophy); Systems engineering; Engineering; Computer network; Business","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002886407,0.0002252581,0.0003353778,0.0001130204,0.000114248,0.00004505554,0.0007748949,0.0001817757,0.00002719016],"category_scores_gemma":[0.0001050795,0.0001962949,0.00004812815,0.0002051559,0.0001588066,0.0003502156,0.0002122387,0.0002269756,0.00006975678],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001302082,"about_ca_system_score_gemma":0.00002028683,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007189777,"about_ca_topic_score_gemma":0.001193977,"domain_scores_codex":[0.9975824,0.001101387,0.0004745316,0.0002403357,0.0001658961,0.0004354986],"domain_scores_gemma":[0.9969518,0.001026798,0.00006789487,0.001763126,0.00006765359,0.0001227404],"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":[0.00006685231,0.0009673863,0.0314995,0.0002805003,0.001114644,0.0000431223,0.003157275,0.302139,0.04456881,0.01929054,0.02533179,0.5715405],"study_design_scores_gemma":[0.00702945,0.0001961426,0.1903914,0.000946299,0.0001385158,0.00002965138,0.0002071796,0.1799714,0.002812171,0.004760657,0.6107879,0.002729201],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7558481,0.145072,0.0440457,0.01358025,0.01896857,0.003115474,0.0001750919,0.002332774,0.01686213],"genre_scores_gemma":[0.9550766,0.04358065,0.0005141487,0.0000387386,0.0004526567,0.0001042328,0.00001078981,0.00005759549,0.0001646289],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5854561,"threshold_uncertainty_score":0.8004675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04721845006545004,"score_gpt":0.2594894926855256,"score_spread":0.2122710426200756,"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."}}