{"id":"W2517685286","doi":"10.1109/infcomw.2016.7562247","title":"On the flexibility of data fulfillment locations in data-intensive distributed systems","year":2016,"lang":"en","type":"article","venue":"","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Cache; Provisioning; Distributed computing; Latency (audio); Flexibility (engineering); Cache pollution; Cache algorithms; Scheme (mathematics); Cache coloring; CPU cache; Computer network","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":[0.002589523,0.0006004067,0.0009233042,0.0008578434,0.00135339,0.002265196,0.001955505,0.0008570542,0.001588292],"category_scores_gemma":[0.01272107,0.0004923489,0.0004919978,0.001603861,0.002036422,0.004334291,0.001702712,0.001233434,0.0002240501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001470615,"about_ca_system_score_gemma":0.0008484065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001776922,"about_ca_topic_score_gemma":0.001333813,"domain_scores_codex":[0.9977748,0.000685939,0.0001625436,0.0005568623,0.0004429336,0.0003768206],"domain_scores_gemma":[0.9914325,0.00509422,0.0009383037,0.001524835,0.000566618,0.0004434659],"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":[0.001195399,0.0001649395,0.007194949,0.0003116835,0.00008699597,0.0005297277,0.0007843161,0.7310624,0.01826065,0.1702657,0.001259441,0.06888382],"study_design_scores_gemma":[0.00003326554,0.0001632051,0.001590126,0.00003152977,0.00004265811,0.0001830905,0.0002413524,0.9394427,0.00414099,0.05171497,0.00237322,0.00004289849],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3718957,0.00283486,0.6140415,0.001357572,0.0001151878,0.0001107962,0.0002248361,0.0004024745,0.009017105],"genre_scores_gemma":[0.9863257,0.0003415488,0.01260213,0.00003455098,0.00003784455,0.00002761417,0.00003250004,0.00002559821,0.00057262],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002589523,"threshold_uncertainty_score":0.01369488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1010552446188255,"score_gpt":0.3018434909082253,"score_spread":0.2007882462893998,"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."}}