{"id":"W3034988621","doi":"10.1109/noms47738.2020.9110469","title":"CONTRAST: Container-based Transcoding for Interactive Video Streaming","year":2020,"lang":"en","type":"article","venue":"","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Transcoding; Computer science; Quality of experience; Real-time computing; Frame rate; Codec; Video post-processing; Frame (networking); Interactive video; Multimedia; Video processing; Multiview Video Coding; Computer network; Quality of service; Video tracking; Operating system; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000571197,0.0007295975,0.0004582345,0.0004358281,0.0003204024,0.0008242665,0.001896908,0.0005104849,0.002192072],"category_scores_gemma":[0.002699793,0.0002448841,0.0005154997,0.0002415617,0.0005236534,0.001036277,0.001254354,0.00138875,0.0008154849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000586029,"about_ca_system_score_gemma":0.0007085881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003630502,"about_ca_topic_score_gemma":0.00313876,"domain_scores_codex":[0.9995223,0.00005162252,0.00003839733,0.00009422652,0.000232711,0.00006081245],"domain_scores_gemma":[0.9991079,0.0001806947,0.00005863456,0.0002485218,0.0003199649,0.00008431467],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001347104,0.0003971146,0.004375687,0.0004429356,0.0001848502,0.001490069,0.00104353,0.04909535,0.400154,0.02102804,0.04869219,0.4717491],"study_design_scores_gemma":[0.0001268625,0.0004332007,0.002360828,0.00008322395,0.00008388628,0.001307704,0.0001520651,0.7264256,0.2112938,0.006401088,0.05119578,0.0001359797],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0264232,0.0004537369,0.9253868,0.0001325812,0.0002093068,0.0003063809,0.0002269687,0.04246368,0.004397414],"genre_scores_gemma":[0.3621966,0.0003652753,0.6239913,0.0002848084,0.00007272162,0.0002976467,0.001270346,0.00440908,0.007112224],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003630502,"threshold_uncertainty_score":0.007333219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03830810476305537,"score_gpt":0.2693060089819388,"score_spread":0.2309979042188834,"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."}}