{"id":"W2135247072","doi":"10.1186/s40493-014-0010-0","title":"A trust-based framework for vehicular travel with non-binary reports and its validation via an extensive simulation testbed","year":2014,"lang":"en","type":"article","venue":"Journal of Trust Management","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Education, India","keywords":"Testbed; Computer science; Intelligent transportation system; Path (computing); Trustworthiness; Binary decision diagram; Vehicular ad hoc network; Data mining; Wireless ad hoc network; Algorithm; Computer network; Transport engineering; Computer security; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006671153,0.0002177341,0.0003224783,0.0002084744,0.00008083057,0.00007528862,0.000101694,0.0001083124,0.00001124787],"category_scores_gemma":[0.00005540507,0.0001910707,0.00008670913,0.000165773,0.00001579483,0.0003085067,0.00001647259,0.0002119223,0.000001356329],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009637776,"about_ca_system_score_gemma":0.00001212747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.686529e-7,"about_ca_topic_score_gemma":7.930951e-7,"domain_scores_codex":[0.9986227,0.00004398008,0.0005136281,0.0002076625,0.0003574503,0.0002546022],"domain_scores_gemma":[0.9989089,0.0001093122,0.0003409121,0.0002668029,0.000226922,0.0001470857],"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":[0.0001145607,0.00005615574,0.0002540276,0.0002069665,0.0001618336,0.0001942385,0.00008865571,0.9928082,0.0004493621,0.000132477,0.0001110726,0.005422443],"study_design_scores_gemma":[0.001101159,0.0005821206,0.008812379,0.0002665231,0.0003145695,0.00008073632,0.00007413512,0.9859571,0.0008126813,0.0009199637,0.0008452329,0.0002334212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4385141,0.00008865006,0.5605908,0.0000831099,0.0001825036,0.0004413048,8.24594e-7,0.00003064964,0.00006805736],"genre_scores_gemma":[0.9561142,0.00001719981,0.04336643,0.0001173597,0.0002665882,0.00002205713,0.00001509202,0.00005725038,0.00002379686],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5176002,"threshold_uncertainty_score":0.7791638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008548800005193381,"score_gpt":0.2331116036565539,"score_spread":0.2245628036513605,"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."}}