{"id":"W2105170022","doi":"10.1093/comjnl/bxu109","title":"Dataflow-Based Scheduling for Scientific Workflows in HPC with Storage Constraints","year":2014,"lang":"en","type":"article","venue":"The Computer Journal","topic":"Distributed and Parallel Computing Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University; University of New Brunswick","funders":"","keywords":"Dataflow; IBM; Workflow; Computer science; Scheduling (production processes); Library science; Operating system; Database; 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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.003419265,0.0001887021,0.0002564676,0.0001817726,0.0006234189,0.001679307,0.002028921,0.00004800698,0.000005052063],"category_scores_gemma":[0.0000347953,0.0001235589,0.00008963733,0.0005173167,0.000193489,0.0003141014,0.0001688781,0.0003945087,0.00001918706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005548717,"about_ca_system_score_gemma":0.0002375758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002303091,"about_ca_topic_score_gemma":0.000007052234,"domain_scores_codex":[0.9979926,0.0003143366,0.000397412,0.0003986759,0.0003912866,0.0005056331],"domain_scores_gemma":[0.9982869,0.0004477438,0.0002417653,0.0006956478,0.0001831094,0.0001448509],"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":[0.00005149261,0.0001511873,0.001295657,0.00004531182,0.00004809651,0.00004949761,0.001019116,0.8658255,0.00008938146,0.01878702,0.004031333,0.1086064],"study_design_scores_gemma":[0.001378749,0.0001463792,0.0006173135,0.0002509842,0.000006062037,0.0002384611,0.00001182053,0.9864811,0.00002665298,0.00170507,0.0089409,0.0001965215],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05374858,0.00005468363,0.9427442,0.00108031,0.00192471,0.0002341369,0.000004838631,0.00007420551,0.0001343626],"genre_scores_gemma":[0.796751,3.145674e-7,0.2023468,0.0003319686,0.0005197126,0.000005456995,0.00000770984,0.00001034148,0.00002672842],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7430024,"threshold_uncertainty_score":0.999357,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0227176618995806,"score_gpt":0.2383109939341042,"score_spread":0.2155933320345236,"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."}}