{"id":"W2168724353","doi":"10.1109/aina.2008.77","title":"PACONET: imProved&amp;#x0A0;&amp;#x0A0;Ant Colony Optimization Routing Algorithm for Mobile Ad Hoc NETworks","year":2008,"lang":"en","type":"article","venue":"","topic":"Mobile Ad Hoc Networks","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Destination-Sequenced Distance Vector routing; Computer network; Ad hoc On-Demand Distance Vector Routing; Ant colony optimization algorithms; Wireless Routing Protocol; Optimized Link State Routing Protocol; Dynamic Source Routing; Distance-vector routing protocol; Link-state routing protocol; Distributed computing; Mobile ad hoc network; Routing protocol; Wireless ad hoc network; Routing (electronic design automation); Algorithm; Wireless; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003670135,0.0006349691,0.0006914762,0.0006034355,0.0004266186,0.0007291537,0.001608837,0.0008227736,0.006015416],"category_scores_gemma":[0.001606997,0.0002101158,0.0003439324,0.0007391453,0.0002826392,0.0008165233,0.0006638299,0.00101783,0.001817755],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003160365,"about_ca_system_score_gemma":0.0007007833,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002571117,"about_ca_topic_score_gemma":0.003430083,"domain_scores_codex":[0.9996607,0.00005816062,0.0000140371,0.00004078957,0.0001935301,0.00003278518],"domain_scores_gemma":[0.9996362,0.00008278877,0.00004259126,0.00004929443,0.0001621108,0.00002714898],"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":[0.0003898644,0.0002760791,0.001549709,0.0003557234,0.00009958295,0.0005135717,0.00009286473,0.3298472,0.01803743,0.01266722,0.04537903,0.5907917],"study_design_scores_gemma":[0.00009298526,0.0001568888,0.0005494099,0.00002110669,0.00002333634,0.0002547387,0.00001314765,0.9631464,0.004461803,0.002373367,0.02888549,0.00002135422],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02872315,0.001878182,0.9275246,0.0008138365,0.0007103406,0.0003644881,0.0004701617,0.006424946,0.0330903],"genre_scores_gemma":[0.2331169,0.001183489,0.7244857,0.0006495411,0.0001719781,0.0006741607,0.001490188,0.0006774369,0.03755059],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006015416,"threshold_uncertainty_score":0.0201236,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01881941875000288,"score_gpt":0.2472265425136042,"score_spread":0.2284071237636014,"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."}}