{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001472028,0.0004085032,0.000629996,0.0005393277,0.0009363693,0.001922438,0.001429741,0.000467281,0.003092122],"category_scores_gemma":[0.003651515,0.0002675701,0.0002757414,0.0005042086,0.000676329,0.0007647899,0.0009229414,0.0011673,0.0009302314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008365054,"about_ca_system_score_gemma":0.002454905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003368215,"about_ca_topic_score_gemma":0.003036048,"domain_scores_codex":[0.9992366,0.0001100134,0.00004364383,0.0001226636,0.0003340379,0.0001531236],"domain_scores_gemma":[0.998126,0.0004485986,0.0001267155,0.0003820176,0.0006014199,0.0003152525],"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":[0.004262514,0.00113679,0.004766501,0.0003395877,0.0001263428,0.0006005183,0.0004268473,0.336461,0.08028089,0.1235891,0.02347271,0.4245372],"study_design_scores_gemma":[0.0001166368,0.0001311464,0.0002778884,0.000006105553,0.00001672878,0.0000682582,0.00001843691,0.9727861,0.01114166,0.01184846,0.003573347,0.00001523759],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1042447,0.0006567074,0.8674796,0.0006676486,0.0006620702,0.0002168652,0.0001381219,0.005630426,0.02030377],"genre_scores_gemma":[0.9049318,0.0001726738,0.08382048,0.0002218126,0.0001977503,0.00007807957,0.0001508988,0.0002631547,0.0101633],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003368215,"threshold_uncertainty_score":0.01034415,"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."}}