{"id":"W4308196315","doi":"10.3233/jhs-222018","title":"Double Q-learning based routing protocol for opportunistic networks","year":2022,"lang":"en","type":"article","venue":"Journal of High Speed Networks","topic":"Opportunistic and Delay-Tolerant Networks","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Reinforcement learning; Computer network; Routing protocol; Node (physics); Overhead (engineering); Zone Routing Protocol; Protocol (science); Hop (telecommunications); Estimator; Wireless Routing Protocol; Routing (electronic design automation); Distributed computing; Artificial intelligence; Statistics; Mathematics","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":[0.002904217,0.0003567851,0.0006941373,0.0002463259,0.001069482,0.0003522325,0.001546701,0.000154773,0.0002371117],"category_scores_gemma":[0.000021418,0.0003302154,0.0004041889,0.0007180831,0.00006415596,0.0004133105,0.0004609395,0.001602681,0.000001661175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002096759,"about_ca_system_score_gemma":0.0005060782,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001236066,"about_ca_topic_score_gemma":0.00000121505,"domain_scores_codex":[0.9962078,0.0003009073,0.001344084,0.0004505822,0.0008275643,0.0008690538],"domain_scores_gemma":[0.9961784,0.000703291,0.001809929,0.0004882108,0.0003987679,0.000421394],"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":[0.0009020202,0.0001910686,0.000258676,0.0000201564,0.00007107542,0.0003496354,0.00005705033,0.9393123,0.000005134534,0.009184094,0.02202815,0.02762059],"study_design_scores_gemma":[0.005016043,0.001180481,0.0000462335,0.0000915581,0.00005069661,0.0003229165,0.00004516434,0.9498693,0.000003920395,0.0006366167,0.04237365,0.000363379],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0001006208,0.00003256484,0.9798014,0.0006040218,0.001649183,0.01684046,0.000002578988,0.0001116556,0.0008575586],"genre_scores_gemma":[0.9602688,0.000005428135,0.02480074,0.001332336,0.002812338,0.009626279,0.00002726721,0.00008269851,0.001044134],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9601682,"threshold_uncertainty_score":0.999915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04024305745472528,"score_gpt":0.2874877311948438,"score_spread":0.2472446737401185,"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."}}