{"id":"W2009015308","doi":"10.5539/nct.v2n2p29","title":"A Fast Raptor Codes Decoding Strategy for Real-Time Communication Systems","year":2013,"lang":"en","type":"article","venue":"Network and Communication Technologies","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Decoding methods; Computer science; Sequential decoding; List decoding; Algorithm; Raptor code; Berlekamp–Welch algorithm; Scheme (mathematics); Process (computing); Gaussian; Theoretical computer science; Mathematics; Concatenated error correction code; Block code","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005666375,0.001109715,0.0006039361,0.0009590682,0.0006749362,0.000818268,0.0008938815,0.0009340009,0.001522417],"category_scores_gemma":[0.001743138,0.0003378593,0.0003998314,0.0008136569,0.0006917883,0.001168123,0.0007271023,0.0008879501,0.00106343],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005007204,"about_ca_system_score_gemma":0.001306426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001567034,"about_ca_topic_score_gemma":0.001741271,"domain_scores_codex":[0.9992759,0.0002512193,0.0000384832,0.0000911142,0.0002803022,0.0000630537],"domain_scores_gemma":[0.9991856,0.000279546,0.00008666977,0.0001838637,0.0002404869,0.00002372484],"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":[0.0002198804,0.00007330652,0.0004514609,0.000276941,0.00008495057,0.0002922247,0.0003752565,0.3424926,0.06041056,0.1332199,0.006413525,0.4556892],"study_design_scores_gemma":[0.00004203031,0.0001403832,0.0001108078,0.00003288364,0.00002072556,0.0004354737,0.00003438098,0.9331756,0.03261086,0.02408742,0.009257572,0.00005171888],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003771408,0.0002480853,0.9942679,0.00008282917,0.00002694216,0.00003265164,0.00002821081,0.0003294097,0.001212415],"genre_scores_gemma":[0.1805641,0.0005945119,0.814809,0.0001393767,0.00006712451,0.0001598846,0.0001399956,0.00008588206,0.003440198],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001567034,"threshold_uncertainty_score":0.005093038,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0254761651738884,"score_gpt":0.2686175593594082,"score_spread":0.2431413941855198,"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."}}