{"id":"W2740628997","doi":"10.1109/icc.2017.7996756","title":"Performance evaluation of movement prediction techniques for vehicular networks","year":2017,"lang":"en","type":"article","venue":"","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Else Kröner-Fresenius-Stiftung","keywords":"Computer science; Vehicular ad hoc network; Intelligent transportation system; Mobility model; Wireless ad hoc network; Wireless sensor network; Field (mathematics); Wireless; State (computer science); Wireless network; Computer network; Real-time computing; Telecommunications; Transport engineering; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004934615,0.001494917,0.001161786,0.00214549,0.000766272,0.0008259297,0.001857048,0.0009747578,0.0008069175],"category_scores_gemma":[0.01892949,0.0002856374,0.0004667446,0.002497572,0.0005185137,0.001736842,0.000969516,0.0007388674,0.0002719979],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002053286,"about_ca_system_score_gemma":0.001198258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01822023,"about_ca_topic_score_gemma":0.01053846,"domain_scores_codex":[0.9966159,0.001190715,0.0002189029,0.0004367155,0.001127504,0.0004101808],"domain_scores_gemma":[0.9849612,0.01040277,0.0008754186,0.001118056,0.002393519,0.0002489928],"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":[0.0007117218,0.0002097637,0.007348236,0.0001637594,0.0001258032,0.00005578311,0.00006587592,0.8558753,0.001531297,0.001551563,0.002081653,0.1302793],"study_design_scores_gemma":[0.00001317513,0.0002061074,0.001181966,0.00001156393,0.00002402956,0.00004440457,0.00003668504,0.9967885,0.0009831218,0.0004306101,0.0002692933,0.00001048109],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7497614,0.0131149,0.2185265,0.001133121,0.0005545509,0.0004133365,0.00123468,0.003804823,0.0114567],"genre_scores_gemma":[0.977778,0.00120083,0.01944684,0.00003985234,0.00004024005,0.00005085907,0.0007433924,0.0000462927,0.0006536902],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01822023,"threshold_uncertainty_score":0.0362283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01799157491423687,"score_gpt":0.2513279651238904,"score_spread":0.2333363902096536,"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."}}