{"id":"W2290852076","doi":"10.1109/glocom.2015.7417852","title":"Movement Prediction in Vehicular Networks","year":2015,"lang":"en","type":"article","venue":"2015 IEEE Global Communications Conference (GLOBECOM)","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Kalman filter; Reliability (semiconductor); Hidden Markov model; Movement (music); Reduction (mathematics); Filter (signal processing); Extended Kalman filter; Markov chain; Artificial intelligence; Machine learning; Power (physics); Mathematics","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":[0.0004840922,0.0005527557,0.0005328381,0.0005876743,0.0004783591,0.0005280521,0.0007573947,0.0004756076,0.0004763789],"category_scores_gemma":[0.001803293,0.0003326059,0.0003248103,0.000835394,0.0002553789,0.0008310498,0.0005410157,0.0005437933,0.0002116041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005970601,"about_ca_system_score_gemma":0.0006190715,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02934207,"about_ca_topic_score_gemma":0.01729031,"domain_scores_codex":[0.9997066,0.00006793417,0.00002175481,0.00006837486,0.0000933655,0.00004200085],"domain_scores_gemma":[0.9995491,0.0002037563,0.00006562933,0.00004014596,0.0001214583,0.00001987509],"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":[0.00002911771,0.00001209052,0.002892117,0.00002723315,0.00001483718,0.00005379005,0.00002327039,0.9609879,0.0005951578,0.00136994,0.0007605501,0.03323406],"study_design_scores_gemma":[0.000001448649,0.000006028339,0.0004316578,0.000003366395,0.000003303673,0.00001109973,0.00001140142,0.9979215,0.0002490547,0.001043028,0.0003149241,0.000003177799],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1382318,0.001721579,0.8527733,0.0005498062,0.0002234913,0.00007982144,0.0004918069,0.001414654,0.004513793],"genre_scores_gemma":[0.968725,0.0009272419,0.02850544,0.0000385731,0.00003697907,0.00004165814,0.0003909134,0.00002790954,0.001306266],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02934207,"threshold_uncertainty_score":0.05834252,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04307966333898192,"score_gpt":0.2713759324600797,"score_spread":0.2282962691210978,"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."}}