{"id":"W2117147358","doi":"10.1109/icc.1991.162274","title":"Efficient ARQ schemes for point-to-multipoint communication","year":2002,"lang":"en","type":"article","venue":"","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Institute for Materials Science","keywords":"Additive white Gaussian noise; Decoding methods; Computer science; Channel (broadcasting); Algorithm; Automatic repeat request; Selective Repeat ARQ; Hybrid automatic repeat request; Rayleigh fading; Forward error correction; Binary symmetric channel; Error detection and correction; Throughput; Binary number; Fading; Computer network; Mathematics; Wireless; Telecommunications; Arithmetic; Low-density parity-check code; 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":[0.000388154,0.0001053746,0.0001089713,0.0001189854,0.0001451483,0.00009655944,0.001108231,0.00004132192,0.00003074175],"category_scores_gemma":[0.0002292263,0.00009661624,0.00006346611,0.0002867657,0.00001950533,0.00009422885,0.0003868496,0.00008796741,0.0001450771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006182252,"about_ca_system_score_gemma":0.000007258795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002701475,"about_ca_topic_score_gemma":0.00001669142,"domain_scores_codex":[0.9990941,0.00003813344,0.0001983343,0.000288995,0.0001535343,0.0002269346],"domain_scores_gemma":[0.9983983,0.0002215306,0.00005974329,0.001103578,0.0001397766,0.00007705611],"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":[0.0000164963,0.0008168356,0.0005325068,0.00005496665,0.0000338879,0.0000026608,0.008332424,0.001561487,0.01414429,0.3828163,0.1218336,0.4698545],"study_design_scores_gemma":[0.0001830167,0.0001092462,0.0001000523,0.00003344555,0.000002326726,0.00000681269,0.00003879621,0.9416374,0.03502111,0.001545776,0.02112349,0.0001985506],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008765734,0.00007625126,0.9761686,0.005275742,0.0001175673,0.0005628003,5.952061e-7,0.00124992,0.007782842],"genre_scores_gemma":[0.4956846,0.000002347717,0.503075,0.0005927333,0.00001006596,0.00008853828,4.003817e-7,0.000006757497,0.0005395717],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9400759,"threshold_uncertainty_score":0.3939896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0346465515536406,"score_gpt":0.277372398854377,"score_spread":0.2427258473007364,"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."}}