{"id":"W1533292482","doi":"10.1007/11745693_34","title":"Effective Dynamic Replica Maintenance Algorithm for the Grid Environment","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Distributed and Parallel Computing Systems","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Replica; Computer science; Latency (audio); Grid; Performance metric; Distributed computing; Metric (unit); Replication (statistics); Response time; Data grid; Parallel computing; Grid computing; Algorithm; Real-time computing; Operating system; Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001476847,0.0006412819,0.0005984938,0.0002787012,0.0005567351,0.0005995619,0.004410737,0.0002845545,0.000004283469],"category_scores_gemma":[0.00004883483,0.0004611571,0.0002828748,0.0003470853,0.0007010719,0.0002339638,0.001194559,0.0007016164,0.00004679885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005865998,"about_ca_system_score_gemma":0.0002285615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004545614,"about_ca_topic_score_gemma":0.00001515074,"domain_scores_codex":[0.995585,0.00005470553,0.0006055512,0.001969342,0.0008833937,0.0009019945],"domain_scores_gemma":[0.9954963,0.00165998,0.0004450828,0.002123115,0.000154701,0.0001208749],"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":[0.000003246438,0.00002363402,0.000003443358,0.00002544093,0.00002149626,0.00002761725,0.0001321382,0.1404384,0.00001406254,0.00302193,0.0005711038,0.8557175],"study_design_scores_gemma":[0.0003227963,0.0001943627,0.0001222604,0.0002505119,0.00001334016,0.00008637782,9.48499e-8,0.921985,0.00007170659,0.03128304,0.04511489,0.0005556572],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000003961793,0.001054,0.9917938,0.0009103841,0.003240985,0.001739214,0.00006656699,0.0001772603,0.001013871],"genre_scores_gemma":[0.06601698,0.0001164284,0.9254835,0.001882411,0.002557439,0.0003594522,0.00008729415,0.0001228712,0.003373647],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8551618,"threshold_uncertainty_score":0.999784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00673495869084776,"score_gpt":0.2187025594962396,"score_spread":0.2119676008053918,"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."}}