{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005239292,0.000932203,0.0007891693,0.0009658617,0.001496612,0.001054728,0.001173571,0.0006259782,0.001043436],"category_scores_gemma":[0.0008111526,0.0003555786,0.0006280045,0.001810073,0.0003881988,0.001314425,0.0007334582,0.0005310063,0.0002897539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001692708,"about_ca_system_score_gemma":0.003309714,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0110702,"about_ca_topic_score_gemma":0.008480033,"domain_scores_codex":[0.999458,0.0000951981,0.00004500401,0.0001610635,0.0001414819,0.0000992363],"domain_scores_gemma":[0.9997041,0.00003324266,0.00003378951,0.0000362915,0.0001283962,0.00006420292],"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":[0.0005112042,0.0004326238,0.003612759,0.0003492979,0.00008848056,0.0006443292,0.0004114714,0.5671788,0.06994191,0.03447155,0.007451326,0.3149064],"study_design_scores_gemma":[0.00005138217,0.0001438304,0.0007562331,0.00001093765,0.00003280518,0.0001381819,0.0001588245,0.9767536,0.01140815,0.006270953,0.004238862,0.00003621188],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04902426,0.0005084013,0.946119,0.0003037071,0.0001386968,0.0002549211,0.0001081674,0.0005436122,0.002999312],"genre_scores_gemma":[0.6647672,0.0005074826,0.3311792,0.0001142136,0.0000735609,0.0002389942,0.0002268854,0.00007523177,0.002817304],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0110702,"threshold_uncertainty_score":0.02201152,"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."}}