{"id":"W2995274323","doi":"10.1109/tmm.2019.2957991","title":"Intra Coding Strategy for Video Error Resiliency: Behavioral Analysis","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Retransmission; Coding tree unit; Context-adaptive binary arithmetic coding; Coding (social sciences); Lossy compression; Packet loss; Algorithm; Data compression; Intra-frame; Network packet; Real-time computing; Decoding methods; Artificial intelligence; Mathematics; Computer network; Statistics","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":[0.0006530866,0.0006186562,0.0003120709,0.0006689357,0.0002026286,0.0003696562,0.000611641,0.0005119087,0.001259772],"category_scores_gemma":[0.002534265,0.0001672217,0.0002764266,0.0004616362,0.0003638618,0.0008174891,0.0002712314,0.0005891889,0.0002294413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007534134,"about_ca_system_score_gemma":0.000569705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002850214,"about_ca_topic_score_gemma":0.002057607,"domain_scores_codex":[0.999496,0.000107292,0.0000172837,0.00005449755,0.0002648371,0.00006003839],"domain_scores_gemma":[0.9988593,0.0005065461,0.0001523478,0.0001018746,0.0003524728,0.00002757821],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0002963324,0.0002958425,0.005479898,0.0002693642,0.00009349357,0.0003724278,0.0005846409,0.4901366,0.1785884,0.1150337,0.00198519,0.2068641],"study_design_scores_gemma":[0.000002463293,0.00007503089,0.001327792,0.00001300376,0.00001274454,0.000138782,0.0000368174,0.984051,0.01003342,0.003760176,0.0005352356,0.00001342825],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1200041,0.001297111,0.8670136,0.0003711418,0.00003016048,0.0001327526,0.00007900091,0.0002421861,0.01083005],"genre_scores_gemma":[0.9472107,0.0009458939,0.04836624,0.00008120704,0.00001777792,0.0001215837,0.0000830534,0.00006006378,0.003113491],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002850214,"threshold_uncertainty_score":0.005667269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04617294587042454,"score_gpt":0.3165948014549234,"score_spread":0.2704218555844988,"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."}}