{"id":"W2031628231","doi":"10.1145/1868497.1868499","title":"Adaptive routing in mobile ad hoc networks based on decision aid approach","year":2010,"lang":"en","type":"article","venue":"","topic":"Mobile Ad Hoc Networks","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Mobile ad hoc network; Routing (electronic design automation); Adaptation (eye); Limiting; Destination-Sequenced Distance Vector routing; Wireless ad hoc network; Voting; Adaptive quality of service multi-hop routing; Distributed computing; Optimized Link State Routing Protocol; Work (physics); Scale (ratio); Routing protocol; Process (computing); Computer network; Link-state routing protocol; Wireless; Engineering","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":[0.002074298,0.0006366328,0.000850169,0.0007721264,0.000484082,0.001406083,0.001007279,0.0008344043,0.001259461],"category_scores_gemma":[0.002849574,0.0002660308,0.0005649204,0.0009009745,0.0009270224,0.001307283,0.000976919,0.001024328,0.0002254559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005867388,"about_ca_system_score_gemma":0.0007743295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008897078,"about_ca_topic_score_gemma":0.001001836,"domain_scores_codex":[0.9986082,0.0007257829,0.00006614195,0.0001369823,0.0003827789,0.00008016109],"domain_scores_gemma":[0.998398,0.001197944,0.00009396725,0.00005698772,0.0001988924,0.00005416399],"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":[0.0001916181,0.0001717356,0.001185138,0.0004677089,0.0001858967,0.0003718013,0.0002759754,0.7021073,0.004260656,0.1194217,0.001577894,0.1697827],"study_design_scores_gemma":[0.00002841339,0.0001018474,0.0001381458,0.00002720302,0.00003443129,0.00006661045,0.00004955988,0.9593472,0.001123358,0.03621699,0.002848897,0.00001726477],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008552345,0.0005805462,0.9874468,0.0002833436,0.00007156216,0.00007794409,0.00001576593,0.00008424961,0.002887306],"genre_scores_gemma":[0.577243,0.001456792,0.4172721,0.0002247575,0.0001649257,0.0002436047,0.00005753731,0.00002750107,0.003309775],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002074298,"threshold_uncertainty_score":0.01097006,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01159826589304243,"score_gpt":0.232411400452648,"score_spread":0.2208131345596056,"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."}}