{"id":"W2034937481","doi":"10.1109/tvt.2014.2380827","title":"Cooperative ARQ-Based Energy-Efficient Routing in Multihop Wireless Networks","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Retransmission; Computer network; Computer science; Energy consumption; Physical layer; Relay; Automatic repeat request; Transmission (telecommunications); Hybrid automatic repeat request; Cooperative diversity; Routing protocol; Efficient energy use; Wireless; Distributed computing; Routing (electronic design automation); Channel (broadcasting); Engineering; Fading; Network packet; Telecommunications","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":[],"consensus_categories":[],"category_scores_codex":[0.0003806171,0.0002328485,0.0002813166,0.000573906,0.0003911964,0.00006947071,0.0009829552,0.0002633129,0.00001238376],"category_scores_gemma":[0.00001687482,0.0002310942,0.00008581996,0.001829698,0.0001539205,0.00009432776,0.00001590781,0.0006614781,0.00001828947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001351189,"about_ca_system_score_gemma":0.00005871256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001464025,"about_ca_topic_score_gemma":0.0002629472,"domain_scores_codex":[0.9982365,0.0003105079,0.0003522164,0.0005233183,0.0001709379,0.0004064719],"domain_scores_gemma":[0.9984728,0.0002144442,0.00008954138,0.00100374,0.0001532833,0.00006623478],"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":[0.000007012099,0.0002187008,0.00002305515,0.000001698227,0.00001185365,0.000005989721,0.00007511851,0.7699766,0.001744815,0.04005086,0.000009482887,0.1878748],"study_design_scores_gemma":[0.0007140369,0.0001141575,0.00003692468,0.00007312104,0.000005064357,0.000006581004,0.00002444961,0.9689165,0.02892299,0.00006904259,0.0008747757,0.0002423888],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03169499,0.00009482531,0.9650152,0.002054973,0.0002810207,0.0001711051,7.893159e-7,0.0005060392,0.0001810775],"genre_scores_gemma":[0.9942001,0.00008938707,0.004815688,0.0006784803,0.00001644319,0.0001345982,0.000001742369,0.00001810891,0.0000454826],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9625051,"threshold_uncertainty_score":0.9423746,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0127207975823597,"score_gpt":0.2354540130107428,"score_spread":0.2227332154283831,"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."}}