{"id":"W2079643201","doi":"10.1109/glocom.2011.6133993","title":"Autoregression Models for Trust Management in Wireless Ad Hoc Networks","year":2011,"lang":"en","type":"article","venue":"","topic":"Access Control and Trust","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Norleaf Networks (Canada); University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Autoregressive model; Computer science; Node (physics); Network packet; Time series; Wireless ad hoc network; Bayesian probability; Trust management (information system); Data modeling; Computer network; Wireless; Econometrics; Artificial intelligence; Machine learning; Computer security; Engineering; Telecommunications; Mathematics","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":[0.0003324757,0.00007676857,0.0001156494,0.00005319022,0.0002028135,0.0000356161,0.0002502209,0.00008466536,0.000238088],"category_scores_gemma":[0.000004577219,0.00006020228,0.00004743889,0.0001415031,0.00005271625,0.0002945412,0.00004678452,0.00005496181,0.000007126963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004319051,"about_ca_system_score_gemma":0.00001878308,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000576788,"about_ca_topic_score_gemma":0.002648686,"domain_scores_codex":[0.9991831,0.00003968757,0.0001411589,0.0001817499,0.0001461857,0.0003081794],"domain_scores_gemma":[0.999718,0.00003016205,0.00004413308,0.0001094641,0.00002847355,0.00006981489],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001008179,0.00008968214,0.001712071,0.00000796823,0.00001784633,0.000006990294,0.002944777,0.0002005425,4.806108e-7,0.7068813,0.001462801,0.2865747],"study_design_scores_gemma":[0.005053037,0.0001481604,0.02679642,0.0001533183,0.00008657235,3.391229e-7,0.01495543,0.7137829,0.00003267215,0.1748039,0.06327793,0.0009093335],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.0369399,0.0008088886,0.1662242,0.0006401491,0.0007011235,0.001333028,0.000002633592,0.0002112638,0.7931389],"genre_scores_gemma":[0.991026,0.0004624459,0.001965898,0.0001604032,0.00008085626,0.0001137363,0.000001974801,0.000008014997,0.006180637],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9540861,"threshold_uncertainty_score":0.2606897,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05513094208193946,"score_gpt":0.3005256795892653,"score_spread":0.2453947375073259,"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."}}