{"id":"W4312040304","doi":"10.1109/tcomm.2022.3217574","title":"Age of Information With Hybrid-ARQ: A Unified Explicit Result","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Age of Information Optimization","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Hybrid automatic repeat request; Computer science; Automatic repeat request; Decoding methods; Robustness (evolution); Minification; Real-time computing; Coding (social sciences); Algorithm; Computer network; Mathematics; Telecommunications link","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003516234,0.001210399,0.0008909456,0.001129236,0.0006275988,0.002164176,0.001308972,0.0008385905,0.003135278],"category_scores_gemma":[0.01074523,0.0004648746,0.0007437309,0.001040939,0.002069093,0.003107643,0.001699305,0.001935117,0.0006347954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001745375,"about_ca_system_score_gemma":0.001480183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002310649,"about_ca_topic_score_gemma":0.001740888,"domain_scores_codex":[0.9984033,0.000500407,0.00005730766,0.000205024,0.0005896376,0.0002443163],"domain_scores_gemma":[0.9940322,0.003883921,0.0005096348,0.0003847255,0.001056648,0.0001328716],"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":[0.0001085337,0.00006923382,0.0009589598,0.0003266778,0.00006621696,0.0002303988,0.0003857658,0.5487507,0.006710554,0.4011297,0.00324937,0.03801376],"study_design_scores_gemma":[0.000006165854,0.00004815144,0.0002036262,0.00003177939,0.00002509758,0.00009966665,0.00005155915,0.9527227,0.001108232,0.04401501,0.001666992,0.00002103928],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01133325,0.001771673,0.97663,0.0004213329,0.0001307917,0.00004073296,0.0000671313,0.0001187826,0.009486204],"genre_scores_gemma":[0.8455303,0.004101159,0.1399263,0.0005057848,0.0005801932,0.0001633649,0.0001172931,0.0001936487,0.008881843],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003516234,"threshold_uncertainty_score":0.01859581,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02154446694462322,"score_gpt":0.2362473509092995,"score_spread":0.2147028839646763,"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."}}