{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008777989,0.0002354326,0.0002482016,0.000150407,0.000108456,0.000164839,0.00119707,0.0002231185,0.0000696178],"category_scores_gemma":[0.00004752602,0.0001996424,0.00008629214,0.0008079393,0.00004595115,0.0003492847,0.0003316873,0.0008531237,0.00005418211],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006038481,"about_ca_system_score_gemma":0.00006798392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000102545,"about_ca_topic_score_gemma":0.0001517726,"domain_scores_codex":[0.9978172,0.00009603729,0.0003518931,0.0007749744,0.0004022099,0.0005576373],"domain_scores_gemma":[0.9979333,0.0006126466,0.0001011959,0.001140242,0.00005710813,0.0001555316],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000539486,0.0002002439,0.0006613659,0.000001471334,0.000002951707,0.00001444966,0.0001144782,0.4083893,0.00002208079,0.01129016,0.0007160282,0.5785336],"study_design_scores_gemma":[0.0008005252,0.000211191,0.00111945,0.00003170807,0.000001586281,0.000004218544,0.0000278086,0.994535,0.00005866903,0.0004359597,0.002531898,0.0002419665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01649914,0.0001353143,0.9662586,0.00003580247,0.000641743,0.0005344476,3.970456e-7,0.0002229686,0.01567161],"genre_scores_gemma":[0.7211003,0.0000133768,0.2780705,0.000467238,0.000111072,0.000120352,0.000002560042,0.00001595244,0.00009866898],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7046012,"threshold_uncertainty_score":0.8141179,"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."}}