{"id":"W2354259755","doi":"","title":"A Cloud Data Placement and Task Scheduling Strategy for Scientific Workflow","year":2015,"lang":"en","type":"article","venue":"Jisuanji fangzhen","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Science North","funders":"","keywords":"Workflow; Cloud computing; Computer science; Distributed computing; Scheduling (production processes); Workflow technology; Workflow management system; Workflow engine; Task (project management); Database; Systems engineering; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00158598,0.0001720916,0.0001829606,0.0001196063,0.000316488,0.000953292,0.001615566,0.00005343917,0.000002296419],"category_scores_gemma":[0.00008802464,0.0001509181,0.00003835328,0.0003626382,0.00008310836,0.00009623163,0.001999215,0.0001056689,0.0000316554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005210665,"about_ca_system_score_gemma":0.0001034686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002450821,"about_ca_topic_score_gemma":0.00001647494,"domain_scores_codex":[0.9980031,0.00005233239,0.0002506414,0.0008547845,0.0004052357,0.0004339658],"domain_scores_gemma":[0.9981288,0.00009003664,0.000101204,0.001349559,0.00009577529,0.0002346598],"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":[0.0001731813,0.0006952308,0.001606744,0.0002927931,0.0003952015,0.00007001272,0.01141907,0.1607612,0.0003838693,0.05887025,0.1542839,0.6110486],"study_design_scores_gemma":[0.0008980794,0.0001709302,0.00009643494,0.00004763023,0.00002258605,0.000007037814,0.0004331133,0.8969887,0.00004550233,0.003330405,0.09769998,0.0002596289],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3570641,0.001439572,0.6350549,0.001935697,0.002076732,0.0008041833,0.00002221296,0.0003984036,0.001204264],"genre_scores_gemma":[0.9022391,0.000002689898,0.09466802,0.0002483141,0.0005397564,0.0000258232,0.0000367441,0.00002021341,0.00221929],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7362275,"threshold_uncertainty_score":0.9192617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1155561594608925,"score_gpt":0.3141672553071089,"score_spread":0.1986110958462164,"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."}}