{"id":"W2786609621","doi":"10.1109/pimrc.2017.8292778","title":"Towards QoE-aware HAS video streaming over LTE","year":2017,"lang":"en","type":"article","venue":"","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Quality of experience; Wireless; Context (archaeology); Video quality; Optimization problem; Wireless network; Adaptation (eye); Channel (broadcasting); Real-time computing; Video streaming; Distributed computing; Computer network; Quality of service; Algorithm; Telecommunications","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0003223753,0.0001345707,0.000153835,0.00004460461,0.0006706195,0.001937646,0.001562725,0.00005043557,0.0001701242],"category_scores_gemma":[0.00006025692,0.0001122091,0.00008276439,0.00004939705,0.0000714763,0.001670925,0.0008209936,0.0001103289,0.0001630023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004992201,"about_ca_system_score_gemma":0.0001608353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001454457,"about_ca_topic_score_gemma":0.0001391971,"domain_scores_codex":[0.9987536,0.00003989797,0.0001875094,0.0003696287,0.0003436905,0.0003056869],"domain_scores_gemma":[0.9981424,0.0000371676,0.000120558,0.001527915,0.00007124981,0.0001007558],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000009570142,0.0002823581,0.008524487,0.00007841646,0.000100683,0.0002318733,0.002414867,0.00001866342,0.001441668,0.2870212,0.05340512,0.6464711],"study_design_scores_gemma":[0.00314908,0.000443299,0.4484252,0.000248385,0.00004988411,0.00004875014,0.0005323594,0.1785421,0.06050027,0.03582589,0.2701153,0.002119518],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01012193,0.00002871821,0.9309857,0.009276595,0.0005818176,0.0001151254,0.000002897211,0.0002077831,0.0486794],"genre_scores_gemma":[0.9679754,0.000006692295,0.02603276,0.001476625,0.0001411475,0.000008023721,0.00000143265,0.000007654005,0.004350282],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9578534,"threshold_uncertainty_score":0.9990984,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07164911052440573,"score_gpt":0.3526710242648181,"score_spread":0.2810219137404124,"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."}}