{"id":"W2059145255","doi":"10.1109/mnet.2015.7064900","title":"EMC: Emotion-aware mobile cloud computing in 5G","year":2015,"lang":"en","type":"article","venue":"IEEE Network","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":192,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Cloud computing; Mobile cloud computing; Bottleneck; Mobile computing; Context (archaeology); Big data; Wireless; Utility computing; Personalization; Mobile broadband; Distributed computing; Computer network; World Wide Web; Telecommunications; Cloud computing security; Embedded system; Operating system","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005250288,0.0004205528,0.0004525086,0.0002966402,0.0009243807,0.00155261,0.00112662,0.0006487985,0.001911868],"category_scores_gemma":[0.0007778977,0.0001185645,0.0003005656,0.0006132185,0.0005501481,0.001383307,0.001408983,0.0008577862,0.0004250078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001055095,"about_ca_system_score_gemma":0.0009303361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005681501,"about_ca_topic_score_gemma":0.006624929,"domain_scores_codex":[0.9995555,0.0001087833,0.00002095869,0.00006778601,0.0001257555,0.0001212014],"domain_scores_gemma":[0.9998068,0.000032948,0.00001476415,0.00003985841,0.00006002253,0.00004554569],"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":[0.0005181953,0.00026481,0.003547871,0.000264088,0.0001122463,0.0008800195,0.0004879109,0.1162389,0.02757225,0.3683083,0.07126646,0.4105389],"study_design_scores_gemma":[0.00003658993,0.00007923075,0.001045447,0.00003106964,0.00003488985,0.0002547323,0.0002101112,0.9012277,0.006671944,0.04354668,0.04681777,0.00004393308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0403598,0.002672835,0.9097815,0.003979884,0.000801329,0.0003494537,0.0002008593,0.002293927,0.03956055],"genre_scores_gemma":[0.855401,0.001199506,0.1369499,0.0008582809,0.0002684613,0.0001237941,0.0001867409,0.00008755834,0.00492468],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005681501,"threshold_uncertainty_score":0.01129687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04523380919335297,"score_gpt":0.3205282983850841,"score_spread":0.2752944891917311,"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."}}