{"id":"W2079931854","doi":"10.1109/icc.2009.5305949","title":"Subset Selection in Type-II Hybrid ARQ/FEC for Video Multicast","year":2009,"lang":"en","type":"article","venue":"","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Retransmission; Hybrid automatic repeat request; Automatic repeat request; Computer science; Network packet; Multicast; Forward error correction; Selective Repeat ARQ; Error detection and correction; Algorithm; Go-Back-N ARQ; Computer network; Real-time computing; Decoding methods","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":[0.0006934521,0.0004423253,0.0005668509,0.0002999316,0.000328034,0.0003250585,0.0009117351,0.000366945,0.0005008715],"category_scores_gemma":[0.001273209,0.0001934106,0.0003008162,0.0002347353,0.0003292004,0.0006109739,0.0003826903,0.0003265783,0.0001322687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002414599,"about_ca_system_score_gemma":0.0001971333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005177046,"about_ca_topic_score_gemma":0.000792825,"domain_scores_codex":[0.9994171,0.0002489401,0.00003195163,0.00006017396,0.000197672,0.0000442386],"domain_scores_gemma":[0.9991722,0.0004075706,0.00008864152,0.0001510836,0.0001531226,0.00002740283],"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":[0.0005844023,0.0001800126,0.002336108,0.0001036492,0.0001001079,0.0003267721,0.0002228507,0.6650668,0.06863544,0.01111957,0.001104652,0.2502196],"study_design_scores_gemma":[0.00002306491,0.0001659258,0.0003348612,0.000005253115,0.00001744247,0.0001820975,0.00002122289,0.9845222,0.01192097,0.00211394,0.000680809,0.0000122629],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1053102,0.0003857391,0.8925176,0.00007588862,0.00004271113,0.00006000993,0.00002063044,0.0003032477,0.001283933],"genre_scores_gemma":[0.8003966,0.0001809476,0.1979398,0.0000651161,0.0000444727,0.00008236266,0.00004373509,0.00003994476,0.001207099],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0009117351,"threshold_uncertainty_score":0.003667414,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02458546784871695,"score_gpt":0.2769861443395443,"score_spread":0.2524006764908273,"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."}}