{"id":"W2221180460","doi":"10.1016/j.image.2015.08.007","title":"Dynamic rate adaptation for adaptive video streaming in wireless networks","year":2015,"lang":"en","type":"article","venue":"Signal Processing Image Communication","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Dynamic Adaptive Streaming over HTTP; Testbed; Markov decision process; Real Time Streaming Protocol; Real-time computing; Quality of service; Wireless; Bandwidth (computing); Scalability; Wireless network; Dynamic programming; Quality of experience; Adaptation (eye); Markov process; Computer network; The Internet; Algorithm","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":[0.001745952,0.0001612208,0.0001925752,0.0001344073,0.0002256634,0.0004276049,0.0009487235,0.00007357929,9.947023e-7],"category_scores_gemma":[0.00007177915,0.0001704679,0.00004263688,0.0004678074,0.00007760746,0.002294441,0.0002425215,0.0002417583,0.000004926463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002508127,"about_ca_system_score_gemma":0.000314312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001118016,"about_ca_topic_score_gemma":0.0001584401,"domain_scores_codex":[0.9983429,0.0004283274,0.0004080192,0.0003251931,0.0002073282,0.0002882415],"domain_scores_gemma":[0.9982681,0.0003038203,0.0002955503,0.000565426,0.0004897115,0.00007736895],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001359521,0.0003024439,0.00007247629,0.00009029957,0.00001720291,0.00000501126,0.008705844,0.02428037,0.002985481,0.006804009,0.0002242464,0.9563767],"study_design_scores_gemma":[0.0007292619,0.00009900996,0.0003673263,0.0001990454,0.000008161272,0.00000239959,0.001362117,0.9866279,0.000487202,0.00984204,0.00007345262,0.0002020433],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002960371,0.000824259,0.9942728,0.0009671388,0.00003967641,0.0003781066,0.000001604747,0.0001224526,0.0004335628],"genre_scores_gemma":[0.7866429,0.00002510695,0.2129347,0.0001738109,0.00001533655,0.0001097033,0.00004433189,0.00001384741,0.00004027108],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9623476,"threshold_uncertainty_score":0.695148,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05949641170084471,"score_gpt":0.3316253395278473,"score_spread":0.2721289278270026,"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."}}