{"id":"W38056905","doi":"10.1021/acsami.3c10586","title":"A GoP based FEC technique for packet based video streaming","year":2006,"lang":"en","type":"article","venue":"International Conference on Communications","topic":"Network Traffic and Congestion Control","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Shanghai Jiao Tong University; National Natural Science Foundation of China","keywords":"Computer science; Forward error correction; Network packet; Frame (networking); Lossy compression; Group of pictures; Real-time computing; Data compression; Transmission (telecommunications); Computer network; Algorithm; Decoding methods; Telecommunications; Artificial intelligence","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":[0.0003193077,0.0001597281,0.0001398919,0.0001806283,0.0002618347,0.0002566259,0.002746073,0.00007736664,0.00006955249],"category_scores_gemma":[0.00007353935,0.0001638827,0.0001122011,0.0001953493,0.00009597545,0.0002152456,0.0001490809,0.0001961643,0.00003188231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001002467,"about_ca_system_score_gemma":0.0002754459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008084213,"about_ca_topic_score_gemma":0.000234903,"domain_scores_codex":[0.9987873,0.0001183234,0.0003148735,0.0003100282,0.0002681369,0.0002013241],"domain_scores_gemma":[0.9971672,0.0007507911,0.0001660731,0.001389303,0.0004690968,0.00005754196],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001877522,0.0002429375,0.0001635428,0.000003335934,0.00001481545,9.239307e-7,0.00001229787,0.001032442,0.001277641,0.9505278,0.002205682,0.04449978],"study_design_scores_gemma":[0.0007309705,0.00007701433,0.0005454175,0.00009154853,0.000008198301,0.000001943032,0.00001030759,0.9517418,0.001085499,0.01595161,0.02955326,0.0002024099],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0001922781,0.00003229175,0.9420623,0.02919002,0.0001935841,0.0004926068,0.00005713471,0.0002417544,0.02753804],"genre_scores_gemma":[0.8729126,0.000008167481,0.1245766,0.000981633,0.00007216894,0.0008830647,0.0001580545,0.00001076227,0.0003969538],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9507094,"threshold_uncertainty_score":0.6682941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04927993869160498,"score_gpt":0.309450815710387,"score_spread":0.260170877018782,"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."}}