{"id":"W2018425507","doi":"10.1109/wi-iat.2010.66","title":"Intelligent Agents in Mobile Vehicular Ad-Hoc Networks: Leveraging Trust Modeling Based on Direct Experience with Incentives for Honesty","year":2010,"lang":"en","type":"article","venue":"","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; University of Waterloo","funders":"","keywords":"Honesty; Computer science; Vehicular ad hoc network; Wireless ad hoc network; Incentive; Information exchange; Computer security; Mobile ad hoc network; Intelligent transportation system; Information sharing; World Wide Web; Transport engineering; Wireless; Engineering; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":["open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0005128188,0.000253417,0.0002420869,0.0002113949,0.0001541088,0.0001995578,0.01076283,0.0001287362,0.00001548674],"category_scores_gemma":[0.001555505,0.000203084,0.00005599834,0.0005987122,0.0001018983,0.0006565212,0.00907276,0.0004067435,0.00000452161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009523903,"about_ca_system_score_gemma":0.00005940167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002665549,"about_ca_topic_score_gemma":0.00005850521,"domain_scores_codex":[0.9978895,0.00004565011,0.0003001737,0.0008833191,0.0003339566,0.0005474488],"domain_scores_gemma":[0.9948841,0.0002084576,0.00008790044,0.004663726,0.00007696207,0.00007883481],"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":[0.000182139,0.0005739685,0.01503573,0.00004919932,0.00003020496,0.00006671229,0.0011369,0.8260992,0.0006626259,0.0007590026,0.001906588,0.1534978],"study_design_scores_gemma":[0.0003946786,0.0001324363,0.000267884,0.0000959544,0.000002389698,0.000001830436,0.0001528785,0.9917664,0.004292896,0.001460422,0.001144718,0.0002875145],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3339805,0.0001186009,0.6640851,0.000486926,0.0002754837,0.0004833622,0.000002156111,0.0004426098,0.0001252551],"genre_scores_gemma":[0.7432742,0.0000603482,0.2560802,0.0002063064,0.00001664256,0.0003264609,0.000006371022,0.00001690182,0.00001253038],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4092937,"threshold_uncertainty_score":0.9989417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04057480836688498,"score_gpt":0.2901392351341833,"score_spread":0.2495644267672983,"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."}}