{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006226017,0.001297867,0.001544653,0.001053482,0.0006089447,0.001829373,0.00189096,0.001607532,0.001712375],"category_scores_gemma":[0.02086788,0.0006846664,0.001040721,0.001494407,0.001172658,0.002975703,0.001105087,0.002446236,0.000668724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001539178,"about_ca_system_score_gemma":0.0009473771,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008991596,"about_ca_topic_score_gemma":0.005581138,"domain_scores_codex":[0.9968263,0.001906475,0.000160026,0.0004201132,0.0004722355,0.0002147908],"domain_scores_gemma":[0.9874056,0.01003407,0.001093652,0.0004741286,0.0008538085,0.0001386724],"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":[0.0001049928,0.00005403196,0.002623097,0.0001196198,0.0001938509,0.0001741583,0.0002139354,0.7920726,0.0004442344,0.1604316,0.002101617,0.04146623],"study_design_scores_gemma":[0.000009954987,0.00002318943,0.000254902,0.00001026747,0.00002301336,0.00001882675,0.00001619671,0.9589195,0.00007497951,0.03989888,0.0007362966,0.00001399219],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009639392,0.001268878,0.986767,0.0006270528,0.0001029674,0.00002600636,0.00008912621,0.0002098234,0.001269686],"genre_scores_gemma":[0.8493913,0.004083422,0.1391097,0.0003185926,0.0005369162,0.0003244923,0.0004752164,0.00008762426,0.005672701],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008991596,"threshold_uncertainty_score":0.03292674,"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."}}