{"id":"W1664344055","doi":"10.1109/vetec.1993.508806","title":"Practical implementation of a mobile data link protocol with a Type II hybrid ARQ scheme and code combining","year":2002,"lang":"en","type":"article","venue":"","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Header; Hybrid automatic repeat request; Selective Repeat ARQ; Automatic repeat request; Coding (social sciences); Convolutional code; Error detection and correction; Throughput; Go-Back-N ARQ; Scheme (mathematics); Computer network; Algorithm; Wireless; Decoding methods; Telecommunications link","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":[0.000909091,0.0004708309,0.0003128532,0.0003012909,0.0003391296,0.0008041204,0.001326727,0.000631461,0.001322847],"category_scores_gemma":[0.001182678,0.0002276052,0.0002995943,0.0001932563,0.0006309334,0.0006868014,0.0005183684,0.0006586459,0.0004290131],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004521242,"about_ca_system_score_gemma":0.0005630157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006615168,"about_ca_topic_score_gemma":0.0005539599,"domain_scores_codex":[0.9993298,0.0002315168,0.00003432062,0.00006087041,0.0002595322,0.00008396985],"domain_scores_gemma":[0.9992886,0.0003088731,0.00008516983,0.000139826,0.0001504769,0.00002714106],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005354891,0.0002377337,0.003286956,0.0005532775,0.0001256333,0.00141112,0.0005256762,0.1717832,0.2946611,0.2522675,0.003075844,0.2715365],"study_design_scores_gemma":[0.00009894049,0.001043462,0.0005226635,0.00006024198,0.00005701519,0.001266365,0.00006112265,0.8161117,0.1453191,0.01587721,0.01951392,0.00006825816],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01896196,0.0001343958,0.9768742,0.00009848503,0.00003952442,0.000136466,0.00002179721,0.0008012246,0.002931944],"genre_scores_gemma":[0.6222055,0.0002020679,0.3742285,0.00010166,0.00004441217,0.0002838339,0.00007219044,0.00004681144,0.002815017],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001326727,"threshold_uncertainty_score":0.00480777,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1548488818712409,"score_gpt":0.4344556050823908,"score_spread":0.27960672321115,"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."}}