{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003073458,0.0001235233,0.000137558,0.0004467578,0.0008937706,0.00009038739,0.001924642,0.00002552306,0.00006404223],"category_scores_gemma":[0.000008437504,0.0001280846,0.0000632755,0.00105881,0.00007821141,0.001845177,0.00003524241,0.0003535863,0.00003409134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001379056,"about_ca_system_score_gemma":0.00015008,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007650942,"about_ca_topic_score_gemma":0.00002883621,"domain_scores_codex":[0.9986329,0.0001648643,0.0004958013,0.0001195812,0.0004365245,0.0001502985],"domain_scores_gemma":[0.9970426,0.000178545,0.0003043928,0.002194088,0.0002215465,0.00005884033],"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":[0.00009226577,0.0005171524,0.000004749681,0.00002311834,0.0000758099,0.000001935173,0.00828189,0.8889325,0.0002779589,0.07042453,0.0008478631,0.03052018],"study_design_scores_gemma":[0.002448285,0.000843502,0.0002050437,0.00005690685,0.0000544227,0.0001113749,0.00257689,0.9312108,0.01034562,0.001410301,0.05009243,0.0006444562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0006947953,0.00001059975,0.9867591,0.002055642,0.0001090958,0.0003643069,0.00007367648,0.0002232535,0.009709516],"genre_scores_gemma":[0.8939264,0.00005453944,0.1049749,0.0003888759,0.000002575215,0.0002991073,0.0001019785,0.000008481148,0.0002431099],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8932316,"threshold_uncertainty_score":0.6874251,"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."}}