{"id":"W2154601749","doi":"10.1109/percomw.2005.8","title":"A Lightweight Service Discovery Mechanism for Mobile Ad Hoc Pervasive Environment Using Cross-Layer Design","year":2005,"lang":"en","type":"article","venue":"","topic":"Mobile Ad Hoc Networks","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Service discovery; Computer science; Computer network; Wireless ad hoc network; Mobile ad hoc network; Vehicular ad hoc network; Scalability; Overhead (engineering); Distributed computing; Service layer; Ad hoc wireless distribution service; Service (business); Optimized Link State Routing Protocol; Adaptive quality of service multi-hop routing; Mechanism (biology); Layer (electronics); Network layer; Routing (electronic design automation); Routing protocol; Quality of service; Web service; Telecommunications; Wireless; Database; World Wide Web; Operating system","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003299851,0.0003141073,0.0002721143,0.00006553255,0.0002857731,0.0005359983,0.001092681,0.000150086,0.0001881424],"category_scores_gemma":[0.0000072827,0.0002718683,0.0001457527,0.0002090961,0.00003236509,0.001869486,0.0005095917,0.0001374035,0.0001923625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002585878,"about_ca_system_score_gemma":0.00009895034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001025186,"about_ca_topic_score_gemma":0.00001975149,"domain_scores_codex":[0.9976971,0.00007335236,0.0003820184,0.0008249971,0.0003785143,0.000643999],"domain_scores_gemma":[0.9983946,0.000222596,0.0001457059,0.0009791867,0.00009159647,0.0001662997],"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":[0.0001866296,0.001060351,0.00008425393,0.0001279681,0.0002766346,0.00005452964,0.00405866,0.6244746,0.07402879,0.2081637,0.004249792,0.08323402],"study_design_scores_gemma":[0.0007550524,0.0002435064,0.00002889308,0.00002550174,0.00002422413,0.0000282932,0.00003740628,0.898834,0.07556372,0.004964964,0.01901487,0.0004795439],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02762061,0.000596084,0.9693242,0.0004217503,0.0002671164,0.001374295,0.000006501391,0.0001616787,0.0002278093],"genre_scores_gemma":[0.213656,0.0001893256,0.7791532,0.003243203,0.0003583437,0.0006553375,0.000004917217,0.00005043337,0.002689209],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2743594,"threshold_uncertainty_score":0.9999734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03236000064353097,"score_gpt":0.2651497703232261,"score_spread":0.2327897696796951,"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."}}