{"id":"W3015736707","doi":"10.1109/access.2020.2986580","title":"Power Modeling for Video Streaming Applications on Mobile Devices","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Mitacs","keywords":"Computer science; Toolchain; Real-time computing; Video processing; Broadcasting (networking); Frame (networking); Feature (linguistics); Mobile device; Power (physics); Power consumption; Frame rate; Computer hardware; Computer network; Artificial intelligence","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":[0.0001859359,0.0008076067,0.0005224153,0.0003476587,0.0002397669,0.0005222681,0.0007335354,0.0004444096,0.001985554],"category_scores_gemma":[0.0007791455,0.0002308514,0.0005753765,0.000468613,0.0001669131,0.000711532,0.0002504328,0.0005562638,0.0007952626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004120515,"about_ca_system_score_gemma":0.0002765782,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004138757,"about_ca_topic_score_gemma":0.003306806,"domain_scores_codex":[0.9997981,0.00004629895,0.00001098425,0.00003655433,0.00008603201,0.00002200038],"domain_scores_gemma":[0.9998571,0.00006493275,0.00001493953,0.00001752142,0.00004132938,0.000004152184],"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":[0.00004481097,0.00003284512,0.0007950614,0.00012326,0.00002601958,0.0001455135,0.00006188192,0.9340855,0.009246664,0.004931771,0.001251202,0.04925537],"study_design_scores_gemma":[0.000001569698,0.00002171033,0.0002597064,0.000006023873,0.000005320958,0.00002918289,0.000009257932,0.9964976,0.001042491,0.001185166,0.0009391818,0.000002873046],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03762928,0.0007490747,0.956082,0.0001573612,0.00003880555,0.00008804956,0.0003281783,0.0004497027,0.004477674],"genre_scores_gemma":[0.9142259,0.00183826,0.07402173,0.00010547,0.00007577853,0.0002435491,0.0007969978,0.0002311896,0.008461083],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004138757,"threshold_uncertainty_score":0.008229375,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08307210734619164,"score_gpt":0.38521536204381,"score_spread":0.3021432546976183,"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."}}