{"id":"W1992506382","doi":"10.1016/j.future.2006.12.006","title":"GridX1: A Canadian computational grid","year":2007,"lang":"en","type":"article","venue":"Future Generation Computer Systems","topic":"Distributed and Parallel Computing Systems","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University; University of Toronto; TRIUMF; Canarie; National Research Council Canada; University of Alberta; University of Calgary; University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; CERN","keywords":"Computer science; Grid; Grid computing; Distributed computing; Job scheduler; Load balancing (electrical power); Fault tolerance; Scheduling (production processes); Overhead (engineering); Computer cluster; Large Hadron Collider; Database; Operating system; Cloud computing","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.001193725,0.0003056204,0.0003116696,0.0003963542,0.0005009244,0.0009417251,0.0009957175,0.0001975611,0.000007867233],"category_scores_gemma":[0.000006024623,0.0002954337,0.0001155322,0.0007195419,0.00002257176,0.0004139915,0.0001129243,0.0002133376,0.0002502338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002499775,"about_ca_system_score_gemma":0.0003775824,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005136271,"about_ca_topic_score_gemma":0.005759952,"domain_scores_codex":[0.9971988,0.0001787065,0.0006975167,0.0006650997,0.0005834856,0.0006764336],"domain_scores_gemma":[0.9981431,0.0000783506,0.0002148375,0.0006280489,0.0004158301,0.0005198407],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000002241035,0.00003229163,0.0005234551,0.00002756732,0.00005082426,0.00009005744,0.0006937927,0.1869673,0.0000301981,0.1006707,0.7045345,0.00637711],"study_design_scores_gemma":[0.0002447499,0.00004768128,0.001714874,0.00001971177,0.000002957354,0.0002164651,0.00001901226,0.4865592,0.000009770032,0.00001496119,0.5108773,0.0002732947],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001588021,0.0006141941,0.8615304,0.0008538214,0.133318,0.0003346155,0.0000293167,0.0003522991,0.001379377],"genre_scores_gemma":[0.6467949,0.000004123777,0.08871291,0.001642001,0.2616715,0.0000245309,0.000698361,0.00003644484,0.0004152825],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7728175,"threshold_uncertainty_score":0.9999498,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.014340742692549,"score_gpt":0.2223005355617464,"score_spread":0.2079597928691974,"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."}}