{"id":"W2179069415","doi":"10.1186/s13638-015-0485-0","title":"QoE optimization of video multicast with heterogeneous channels and playback requirements","year":2015,"lang":"en","type":"article","venue":"EURASIP Journal on Wireless Communications and Networking","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada); Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Multicast; Scalable Video Coding; Coding (social sciences); Quality of experience; Scalability; Channel (broadcasting); Video quality; Quality of service; Scheme (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001077278,0.000929846,0.0008363963,0.0003855794,0.0003501444,0.0007546296,0.00077629,0.0007458431,0.0005823009],"category_scores_gemma":[0.002373865,0.0002672376,0.0003519048,0.0003925605,0.0003949911,0.0008146705,0.0007843404,0.0004960083,0.00009498896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008183475,"about_ca_system_score_gemma":0.0005825401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002959402,"about_ca_topic_score_gemma":0.00166978,"domain_scores_codex":[0.9992934,0.0002923076,0.00002047049,0.00008002179,0.0001904615,0.0001232465],"domain_scores_gemma":[0.9992706,0.0004531041,0.00006819432,0.0000244542,0.0001439346,0.00003981637],"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":[0.0001158666,0.0000755485,0.0006974642,0.00005749916,0.00002542285,0.0001323194,0.00004977661,0.9616433,0.01054694,0.004169627,0.0004394614,0.02204678],"study_design_scores_gemma":[0.00000421904,0.00002966571,0.0001005599,0.000001440888,0.000003501612,0.00001399382,0.000007705969,0.9984576,0.0006266605,0.000685859,0.00006632348,0.000002511843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09888927,0.0006210887,0.8973414,0.000293342,0.00003423926,0.00005295808,0.0000404884,0.0001038887,0.002623391],"genre_scores_gemma":[0.9517077,0.0002636452,0.04672488,0.00005450685,0.00003828633,0.00005325527,0.00003238905,0.0000290302,0.001096315],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002959402,"threshold_uncertainty_score":0.005937517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1047641046143644,"score_gpt":0.3306768849202031,"score_spread":0.2259127803058387,"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."}}