{"id":"W2617596100","doi":"10.11591/ijeecs.v6.i1.pp16-25","title":"An Adaptive Cross-Layer Architecture to Optimize QoS Provisioning in MANET","year":2017,"lang":"en","type":"article","venue":"Indonesian Journal of Electrical Engineering and Computer Science","topic":"Mobile Ad Hoc Networks","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ste. Anne's Hospital","funders":"","keywords":"Computer network; Computer science; Adaptive quality of service multi-hop routing; Quality of service; Mobile ad hoc network; Mobile QoS; Wireless ad hoc network; Node (physics); Provisioning; Wireless; Distributed computing; Routing protocol; Optimized Link State Routing Protocol; Routing (electronic design automation); Service (business); Telecommunications; Engineering; Service provider","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001043993,0.0001862284,0.0002949088,0.0005065877,0.0003004833,0.001356508,0.002782599,0.00006326714,6.358608e-7],"category_scores_gemma":[0.00009171057,0.0001567483,0.00004890316,0.0006722762,0.0001144939,0.00139898,0.0003689642,0.0005527519,0.000001193023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007632293,"about_ca_system_score_gemma":0.0001783804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008069031,"about_ca_topic_score_gemma":0.000001080733,"domain_scores_codex":[0.9980534,0.00003519075,0.000395565,0.0004465669,0.0005176433,0.0005516098],"domain_scores_gemma":[0.9984287,0.00009490849,0.0002167568,0.00060499,0.0001672805,0.0004873722],"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":[0.00004202796,0.00005500399,0.001596672,0.000004733079,0.000006555711,0.0002776234,0.000386612,0.7831173,0.001424167,0.003805676,0.00001550521,0.2092681],"study_design_scores_gemma":[0.0004281892,0.0009224971,0.05997703,0.0000936466,0.000001889809,0.0004714522,9.922745e-7,0.9370149,0.0006683801,0.000174895,0.00005126156,0.0001948781],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2379044,0.00006583546,0.7612872,0.0002090859,0.0003938732,0.0000982331,1.311418e-7,0.00002398668,0.00001720777],"genre_scores_gemma":[0.7555885,0.000003834754,0.2440374,0.00009446205,0.0002638803,0.000002881862,2.781632e-8,0.000007132848,0.000001937959],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.517684,"threshold_uncertainty_score":0.9996802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008898932318616624,"score_gpt":0.2524417121613252,"score_spread":0.2435427798427086,"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."}}