{"id":"W2116445224","doi":"10.1109/wowmom.2009.5282475","title":"Weighted-NEAT: An efficient weighted node evaluation scheme with assistant trust mechanisms to secure wireless ad hoc networks","year":2009,"lang":"en","type":"article","venue":"","topic":"Mobile Ad Hoc Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Wireless ad hoc network; Computer network; Node (physics); Correctness; Mobile ad hoc network; Wireless network; Wireless sensor network; Wireless; Scheme (mathematics); Vehicular ad hoc network; Computer security; Distributed computing; Network packet; Telecommunications; Algorithm","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003548671,0.0005201984,0.0007918846,0.000710964,0.0009921337,0.001129186,0.002726742,0.0008417463,0.001794238],"category_scores_gemma":[0.006772134,0.0002839351,0.0006457536,0.0009385411,0.001196403,0.003865094,0.002880606,0.001301542,0.0005326147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001185006,"about_ca_system_score_gemma":0.001398499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008946207,"about_ca_topic_score_gemma":0.001500999,"domain_scores_codex":[0.997934,0.0007747965,0.0001728463,0.000207469,0.0007596591,0.0001512272],"domain_scores_gemma":[0.9976931,0.0006173835,0.0002609192,0.0007280288,0.0005195059,0.0001810026],"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":[0.001018444,0.0003657812,0.001328723,0.0004132348,0.0001480963,0.0004828747,0.0007676259,0.1958493,0.02318805,0.4350557,0.007436407,0.3339457],"study_design_scores_gemma":[0.0001036734,0.0003298719,0.000264479,0.00003778577,0.00007466065,0.0002862639,0.00007479477,0.8728766,0.006848151,0.1049775,0.01406566,0.000060509],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01781609,0.0002460743,0.9776818,0.0002004944,0.0001046945,0.0002424414,0.00004229844,0.0004756992,0.003190404],"genre_scores_gemma":[0.5187491,0.0003832082,0.4721253,0.0001805412,0.00007018292,0.0003266195,0.0001442189,0.00009404446,0.00792695],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003548671,"threshold_uncertainty_score":0.01876736,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01059405396219358,"score_gpt":0.2434155477120922,"score_spread":0.2328214937498986,"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."}}