{"id":"W2016191648","doi":"10.1007/s10586-010-0141-8","title":"Service control with the preemptive parallel job scheduler Scojo-PECT","year":2010,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Distributed and Parallel Computing Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"Technische Universität Berlin","keywords":"Computer science; Predictability; Preemption; Scheduling (production processes); Quality of service; Distributed computing; Job scheduler; Job queue; Service quality; Service (business); Computer network; Mathematical optimization; Operating system","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":[0.001022707,0.0003548898,0.000357442,0.00007677561,0.0006522326,0.0006587754,0.002304635,0.0001254461,0.000008710947],"category_scores_gemma":[0.00004126228,0.0002264894,0.00010536,0.0006845399,0.0001379819,0.0003318557,0.0005843569,0.0007958196,0.000183679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002871784,"about_ca_system_score_gemma":0.0001405609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001027377,"about_ca_topic_score_gemma":0.000180065,"domain_scores_codex":[0.9974725,0.0002647093,0.0003775781,0.0007052149,0.0004796933,0.0007002542],"domain_scores_gemma":[0.9974363,0.0005863679,0.0002924106,0.001161632,0.0003537839,0.0001695369],"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":[0.0006156107,0.0009094515,0.09211864,0.0007100945,0.001884443,0.0003699126,0.05924913,0.5400349,0.005157921,0.1871281,0.05218994,0.05963178],"study_design_scores_gemma":[0.001933267,0.0001124245,0.01709145,0.0001160792,0.00002413359,0.0002320789,0.00026664,0.9730891,0.00002648131,0.0002428131,0.006401027,0.0004645139],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2707124,0.0000518173,0.7192526,0.00499318,0.0008477241,0.0004711733,0.000003133267,0.0003702393,0.00329768],"genre_scores_gemma":[0.9707683,2.495202e-7,0.02510856,0.003331882,0.0006131645,0.00001370093,0.000004356398,0.00002570776,0.0001340306],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.700056,"threshold_uncertainty_score":0.923597,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009007964121247419,"score_gpt":0.2221063133733864,"score_spread":0.213098349252139,"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."}}