{"id":"W2009383337","doi":"10.1109/syscon.2013.6549874","title":"Exploiting excessive resources at data-centres of media content providers using cloud computing","year":2013,"lang":"en","type":"article","venue":"","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"King Abdulaziz City for Science and Technology","keywords":"Cloud computing; Computer science; Exploit; Data center; Utility computing; Profit (economics); Service provider; Cloud computing security; Database; Computer network; Computer security; Operating system; Business; Service (business)","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":[0.002244428,0.0007256489,0.001196149,0.0007301623,0.002286623,0.002713201,0.002674321,0.0008484009,0.001109583],"category_scores_gemma":[0.005643573,0.0004060324,0.0005184776,0.001244043,0.001118774,0.002400848,0.002815385,0.001011368,0.0003045764],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001628486,"about_ca_system_score_gemma":0.00305048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006037645,"about_ca_topic_score_gemma":0.005839548,"domain_scores_codex":[0.9977483,0.0005940621,0.0001434616,0.0004000172,0.0004982896,0.0006158787],"domain_scores_gemma":[0.9959692,0.001409305,0.0006246702,0.0006316782,0.0006659538,0.0006992546],"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":[0.00193182,0.0008289817,0.01597267,0.0003377138,0.0001897284,0.001595692,0.0009571013,0.6104295,0.09862153,0.07038262,0.006106244,0.1926464],"study_design_scores_gemma":[0.00003624105,0.0000793541,0.0009517871,0.00001409452,0.00003663103,0.0001260761,0.0001158744,0.9771443,0.01059727,0.008824782,0.002045435,0.00002805846],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1580873,0.0004256141,0.8341816,0.000784472,0.00009328606,0.0003298602,0.00005201281,0.0009423518,0.005103584],"genre_scores_gemma":[0.9562952,0.00009559886,0.04280451,0.00008164434,0.00005625114,0.00006361834,0.0000277707,0.00002729883,0.0005482004],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006037645,"threshold_uncertainty_score":0.01200503,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0823542103636175,"score_gpt":0.2584065075817193,"score_spread":0.1760522972181018,"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."}}