{"id":"W2027492079","doi":"10.1109/igcc.2011.6008567","title":"Assessing data deduplication trade-offs from an energy and performance perspective","year":2011,"lang":"en","type":"article","venue":"","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Data deduplication; Computer science; Overhead (engineering); Efficient energy use; Energy (signal processing); Distributed computing; Workload; Energy consumption; Real-time computing; Reliability engineering; Database; Operating system; Engineering","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":[0.00348145,0.001131708,0.0009146603,0.001700874,0.0006063649,0.001423421,0.0009355445,0.001090909,0.001415375],"category_scores_gemma":[0.01379495,0.0004045848,0.0005839805,0.002381212,0.000690277,0.003689174,0.0009043583,0.001059307,0.0003565521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001278517,"about_ca_system_score_gemma":0.0005596688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001555235,"about_ca_topic_score_gemma":0.001913208,"domain_scores_codex":[0.9973627,0.0006327225,0.0002389948,0.0003834038,0.000930055,0.0004522765],"domain_scores_gemma":[0.981026,0.0144853,0.001154137,0.001468375,0.001497364,0.0003688094],"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":[0.004124761,0.001654647,0.02935476,0.0007368415,0.0003867213,0.0003041833,0.0002342726,0.7999648,0.04890655,0.005443608,0.002316052,0.1065727],"study_design_scores_gemma":[0.0002713458,0.006281898,0.04255306,0.00008094598,0.0002886564,0.0006655213,0.0008406401,0.8245707,0.1140995,0.006157329,0.004002511,0.0001878058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9774798,0.002507547,0.01397461,0.0003444927,0.0000637698,0.0001081127,0.000511836,0.0003334493,0.004676466],"genre_scores_gemma":[0.9918914,0.0004015061,0.006491747,0.00004742079,0.0000215846,0.00005005029,0.0003955683,0.00008828114,0.0006123445],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00348145,"threshold_uncertainty_score":0.01841187,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06352536362238995,"score_gpt":0.2815196515373443,"score_spread":0.2179942879149544,"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."}}