{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000121887,0.0001037512,0.0001209059,0.00003821692,0.0001569474,0.0004392644,0.001124212,0.00002759107,0.000008510552],"category_scores_gemma":[0.000013153,0.00009767152,0.00005975619,0.0002338126,0.000009119061,0.0009234986,0.0001318434,0.00007775129,0.00003705865],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002289384,"about_ca_system_score_gemma":0.00005171433,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001932645,"about_ca_topic_score_gemma":0.000004601771,"domain_scores_codex":[0.9990228,0.00002267317,0.0001987192,0.000379024,0.0001805579,0.0001962736],"domain_scores_gemma":[0.9992771,0.0001243574,0.0000707662,0.0003494832,0.00009049703,0.00008777868],"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":[0.0001167158,0.001203306,0.001249967,0.0009268021,0.0002648464,0.00002983815,0.0104597,0.4952647,0.01049143,0.1318844,0.01659145,0.3315168],"study_design_scores_gemma":[0.0003107829,0.0001487949,0.0000386143,0.00002564478,0.000008994954,8.233673e-7,0.0001491002,0.964025,0.01159855,0.002556646,0.02088691,0.0002500847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01625776,0.00005725112,0.9806456,0.001409479,0.000113138,0.0005354683,0.000007708885,0.0001467178,0.0008268683],"genre_scores_gemma":[0.9795167,0.000003022808,0.01349033,0.006317445,0.0001533026,0.000491325,0.000003140968,0.000009708851,0.00001500304],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9671553,"threshold_uncertainty_score":0.4235837,"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."}}