{"id":"W2125810107","doi":"10.1109/itcc.2005.244","title":"Replica placement in data grid: considering utility and risk","year":2005,"lang":"en","type":"article","venue":"","topic":"Distributed and Parallel Computing Systems","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Replica; Data grid; Replication (statistics); Distributed computing; Grid; Cache; Grid computing; Latency (audio); Load balancing (electrical power); Data access; Bandwidth (computing); Computer network; Database; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008855481,0.00008048327,0.0001181258,0.00003395448,0.00006082554,0.0001261981,0.0006496381,0.00002765312,0.00001682355],"category_scores_gemma":[0.00005769905,0.0000710954,0.000009469307,0.0001277528,0.00002015782,0.0002760511,0.0008687861,0.00009667539,0.00002446227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000161962,"about_ca_system_score_gemma":0.00002884581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002685734,"about_ca_topic_score_gemma":0.0003582483,"domain_scores_codex":[0.9989067,0.0000738912,0.0002302123,0.0004794916,0.0001208016,0.0001889177],"domain_scores_gemma":[0.9986305,0.000110359,0.00005597437,0.001127782,0.00001365453,0.00006171338],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003004158,0.0004539364,0.4392566,0.0001081478,0.0001022616,0.00006233351,0.00268193,0.01075978,0.00007604311,0.03184824,0.1985475,0.3160731],"study_design_scores_gemma":[0.0003482726,0.00001296543,0.01367623,0.0000185747,0.000002017877,0.00002211022,0.00002973597,0.8074015,0.0000369723,0.000191258,0.178134,0.0001263319],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1957243,0.0004610403,0.7864649,0.001569144,0.0003057969,0.0002603002,0.00004205938,0.0002936636,0.01487876],"genre_scores_gemma":[0.9771659,0.00002767535,0.0224763,0.0001038991,0.00006603411,0.000002081071,0.00001151563,0.000002235283,0.0001443739],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7966417,"threshold_uncertainty_score":0.2899186,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04956848823611644,"score_gpt":0.2808396521608066,"score_spread":0.2312711639246902,"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."}}