{"id":"W3133893905","doi":"10.1016/j.iot.2021.100384","title":"Reinforcement learning-based fuzzy geocast routing protocol for opportunistic networks","year":2021,"lang":"en","type":"article","venue":"Internet of Things","topic":"Opportunistic and Delay-Tolerant Networks","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Computer network; Wireless Routing Protocol; Dynamic Source Routing; Reinforcement learning; Zone Routing Protocol; Routing protocol; Link-state routing protocol; Enhanced Interior Gateway Routing Protocol; Path vector protocol; Destination-Sequenced Distance Vector routing; Static routing; Distributed computing; Routing (electronic design automation); Artificial intelligence","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":[0.0005465007,0.0002867646,0.0005407306,0.0003157152,0.0005145234,0.0004098626,0.0009409393,0.0004351255,0.0007817526],"category_scores_gemma":[0.001307553,0.0001179104,0.0002102853,0.0003118127,0.0004881488,0.0005392537,0.0005659005,0.0005729015,0.00008563876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007641242,"about_ca_system_score_gemma":0.0008581738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006124943,"about_ca_topic_score_gemma":0.006645068,"domain_scores_codex":[0.999757,0.00005212345,0.00001443224,0.00004683594,0.00007790481,0.00005170008],"domain_scores_gemma":[0.9993573,0.0003148633,0.00007351376,0.0000443205,0.0001623748,0.00004763019],"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":[0.0003426778,0.0001523055,0.001415712,0.00008049866,0.00007074868,0.0001993944,0.0001493877,0.8602495,0.01179613,0.02866177,0.002694257,0.09418759],"study_design_scores_gemma":[0.00001261309,0.00003661453,0.0001349489,0.000002084601,0.000008024451,0.00003002795,0.00001051574,0.9947189,0.0007207262,0.003967345,0.0003518549,0.000006303511],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06379104,0.0002741792,0.931877,0.0002472646,0.0001150657,0.00006264737,0.00006310832,0.0003077287,0.00326203],"genre_scores_gemma":[0.9778646,0.00007189096,0.02089344,0.00004451352,0.00001542237,0.00002849223,0.00002968533,0.000008383447,0.001043699],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006124943,"threshold_uncertainty_score":0.0121786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03674563940649983,"score_gpt":0.287546307133943,"score_spread":0.2508006677274432,"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."}}