{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008179239,0.0006509477,0.0008161783,0.0004034092,0.0006864144,0.000871991,0.001250544,0.0009349519,0.003628227],"category_scores_gemma":[0.001481172,0.0002540418,0.0003302135,0.0005563192,0.0005246951,0.0008168237,0.001134598,0.001012911,0.00115344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003092454,"about_ca_system_score_gemma":0.0005789439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001115627,"about_ca_topic_score_gemma":0.001412233,"domain_scores_codex":[0.9993442,0.0001699371,0.00003052714,0.0001248026,0.0002246415,0.0001059184],"domain_scores_gemma":[0.9992199,0.0002762077,0.00003534349,0.0001642347,0.0002621684,0.00004207644],"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":[0.001342672,0.0003118131,0.001745224,0.0002494052,0.0001831723,0.0003894546,0.0002927174,0.09101948,0.2363666,0.0527848,0.00819824,0.6071164],"study_design_scores_gemma":[0.0001858498,0.0003576967,0.0008856236,0.00001631631,0.00008511843,0.0004688672,0.0000460677,0.9630974,0.01886313,0.008632102,0.007274843,0.00008696502],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03423202,0.0007613666,0.9566327,0.0003599022,0.0005872398,0.0001252552,0.00009113899,0.001119041,0.00609143],"genre_scores_gemma":[0.7066672,0.0005464547,0.276926,0.0005602152,0.0004381638,0.0002090148,0.0001936054,0.00007432099,0.01438502],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003628227,"threshold_uncertainty_score":0.01213759,"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."}}