{"id":"W1562031333","doi":"10.1023/a:1008954000541","title":"An Adaptive Hybrid ARQ Scheme","year":2000,"lang":"en","type":"article","venue":"Wireless Personal Communications","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Hybrid automatic repeat request; Algorithm; Rayleigh fading; Throughput; Convolutional code; Bit error rate; Error detection and correction; Channel (broadcasting); Selective Repeat ARQ; Automatic repeat request; Fading; Decoding methods; Telecommunications; Wireless; Transmission (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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001425258,0.0002407161,0.0002274934,0.0001090634,0.000412133,0.00005469396,0.001993997,0.00008355809,0.0005413357],"category_scores_gemma":[0.000006328686,0.0002841463,0.00008755825,0.0002922732,0.0003141418,0.0005495361,0.0001147213,0.0005888276,0.0002511921],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001423301,"about_ca_system_score_gemma":0.00003290688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004545974,"about_ca_topic_score_gemma":0.00006852689,"domain_scores_codex":[0.9988309,0.0001332822,0.0003186216,0.0002126545,0.0002021853,0.0003023655],"domain_scores_gemma":[0.9967991,0.0001323113,0.0000435718,0.002770302,0.0001005008,0.0001542281],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008586603,0.0008641416,0.0005042751,0.00005469054,0.0002340527,0.000007486469,0.004191594,0.004827084,0.0375023,0.06780384,0.00409832,0.8798264],"study_design_scores_gemma":[0.0003622315,0.0001022967,0.0008824885,0.00008776304,0.00002094945,0.00003525164,0.0006203558,0.9193321,0.007689703,0.002171063,0.06794263,0.0007531408],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.782757,0.006567223,0.05851518,0.00195317,0.0001083755,0.001073427,0.0003559322,0.009375985,0.1392937],"genre_scores_gemma":[0.9371873,0.003608569,0.05828418,0.0001402543,0.00003684651,0.0002438536,0.0001933652,0.00007838434,0.0002272455],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9145051,"threshold_uncertainty_score":0.9999611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0249832077146566,"score_gpt":0.2767720381630588,"score_spread":0.2517888304484022,"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."}}