{"id":"W2067577930","doi":"10.1109/clustr.2005.347013","title":"Dynamic Multi-Resource Monitoring for Predictive Job Scheduling with ScoPro","year":2005,"lang":"en","type":"article","venue":"Proceedings","topic":"Distributed and Parallel Computing Systems","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Workload; Distributed computing; Job scheduler; Scheduling (production processes); Grid; Shared resource; Grid computing; Intrusion detection system; Dynamic priority scheduling; Real-time computing; Job queue; Overhead (engineering); Resource (disambiguation); Computer network; Operating system; Cloud computing; Data mining; 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":[0.0003289266,0.0001973476,0.0001957364,0.00009079125,0.0002550964,0.0003572956,0.0007342285,0.00007110793,5.173338e-7],"category_scores_gemma":[0.00005036881,0.0001702196,0.00005607413,0.0003337656,0.00003218105,0.0005893448,0.0001359407,0.0001615517,0.00001519837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001058102,"about_ca_system_score_gemma":0.00004789964,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005462148,"about_ca_topic_score_gemma":9.932451e-7,"domain_scores_codex":[0.9985439,0.000005021318,0.0002290811,0.0005111646,0.0002675833,0.0004433123],"domain_scores_gemma":[0.9992786,0.00004583441,0.0001467703,0.0001488829,0.0002587261,0.0001211901],"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":[0.0008956154,0.001528951,0.2659301,0.003180772,0.001151121,0.00003076542,0.06618619,0.3031928,0.02355918,0.03932391,0.004911005,0.2901095],"study_design_scores_gemma":[0.0009075048,0.0001930198,0.002475042,0.0003614158,0.00001302777,0.00003166045,0.0002317261,0.9874998,0.001304087,0.00007810983,0.006622054,0.0002825841],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1646739,0.0002148984,0.8326027,0.0003851256,0.0001586796,0.0004536737,0.000004001054,0.0005086673,0.0009983529],"genre_scores_gemma":[0.7286118,0.00000159113,0.2707708,0.00003205156,0.0002270859,0.00006849658,0.000001683399,0.00001632905,0.0002701886],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6843069,"threshold_uncertainty_score":0.6941354,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01663310842274929,"score_gpt":0.2593281652369877,"score_spread":0.2426950568142384,"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."}}