{"id":"W2168970974","doi":"10.1109/isit.2010.5513792","title":"Unequal error protection rateless coding design for multimedia multicasting","year":2010,"lang":"en","type":"article","venue":"","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Multicast; Quality of service; Encoder; Computer network; Fountain code; Coding (social sciences); Code (set theory); Online codes; Raptor code; Forward error correction; Decoding methods; Theoretical computer science; Algorithm; Block code; Linear code; Mathematics","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":[0.001150879,0.0001598997,0.0001528025,0.0001333398,0.0002458235,0.0001610885,0.0006195212,0.0001237054,0.000009121345],"category_scores_gemma":[0.00117156,0.0001459991,0.00005992208,0.0002698988,0.00003567354,0.0004421651,0.0001469393,0.0003351692,0.00001949291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003308036,"about_ca_system_score_gemma":0.00006488154,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007388883,"about_ca_topic_score_gemma":0.00006743993,"domain_scores_codex":[0.9986902,0.00006917665,0.0002531557,0.0004441969,0.0001860835,0.0003572209],"domain_scores_gemma":[0.9985889,0.0005516264,0.0001241187,0.0004223774,0.0002258358,0.00008709431],"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":[0.00002733426,0.0000904228,0.0001426301,0.00004000335,0.00001106058,0.000005648391,0.001398714,0.0002886987,0.6795513,0.008363305,0.00034655,0.3097343],"study_design_scores_gemma":[0.0001965569,0.00008978632,0.0000616898,0.00001458515,0.000002664776,0.00001100094,0.00002995632,0.7225196,0.2761246,0.0006564423,0.0001340316,0.0001590839],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0245552,0.000001671655,0.9708465,0.0002684353,0.001117638,0.001135316,5.584422e-7,0.001797442,0.0002772722],"genre_scores_gemma":[0.4949033,1.203366e-7,0.5047128,0.00003349438,0.00007348053,0.0001741446,3.728082e-7,0.00001150774,0.00009085552],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7222309,"threshold_uncertainty_score":0.595367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09463830399280533,"score_gpt":0.3212572633347898,"score_spread":0.2266189593419844,"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."}}