{"id":"W2083812595","doi":"10.1002/ett.4460120104","title":"Hybrid ARQ and optimal signal—to—interference ratio assignment for high—quality data transmission in DS—CDMA","year":2001,"lang":"en","type":"article","venue":"European Transactions on Telecommunications","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Hybrid automatic repeat request; Selective Repeat ARQ; Computer science; Automatic repeat request; Error detection and correction; Go-Back-N ARQ; Code division multiple access; Transmission (telecommunications); Convolutional code; Algorithm; Network packet; Throughput; Bit error rate; Word error rate; Real-time computing; Computer network; Decoding methods; Wireless; Speech recognition; 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":[0.00108981,0.000350604,0.0003503288,0.0003672778,0.0003455135,0.0005435189,0.0005385369,0.0003667359,0.0006517153],"category_scores_gemma":[0.001449133,0.0002446544,0.0002182198,0.0002934005,0.0007694183,0.0005240301,0.0003645579,0.0002626156,0.0001169952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005100914,"about_ca_system_score_gemma":0.00038054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001643523,"about_ca_topic_score_gemma":0.002167036,"domain_scores_codex":[0.9992618,0.0003907919,0.00001937323,0.00005299725,0.000195129,0.00007990746],"domain_scores_gemma":[0.9990169,0.0005713003,0.0001061294,0.00006735605,0.000203517,0.00003477935],"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":[0.001031753,0.0001540519,0.00239492,0.0001401468,0.0001178938,0.0004424983,0.0002834467,0.790955,0.09044201,0.03600449,0.001099234,0.07693447],"study_design_scores_gemma":[0.00004262321,0.0001770044,0.0002424109,0.000002815819,0.00001906661,0.00006241089,0.00001939984,0.9915282,0.005314241,0.002310446,0.0002708011,0.00001050138],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3664787,0.0008022659,0.6285638,0.0001984127,0.00006300615,0.00005387805,0.00002523475,0.0003556932,0.003459077],"genre_scores_gemma":[0.9779578,0.00008173697,0.02145297,0.00001925928,0.00001729604,0.00001415535,0.000006666061,0.000005953678,0.0004441749],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001643523,"threshold_uncertainty_score":0.005763531,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0980398752711771,"score_gpt":0.3439451099290364,"score_spread":0.2459052346578593,"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."}}