{"id":"W777297691","doi":"","title":"Improving Scheduling in Heterogeneous Grid and Hadoop Systems","year":2013,"lang":"en","type":"dissertation","venue":"MacSphere (McMaster University)","topic":"Distributed and Parallel Computing Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Imperial College London","keywords":"Computer science; Grid; Distributed computing; Scheduling (production processes); Grid computing; Database; Parallel computing; Engineering; Operations management; Geography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001546493,0.0004013889,0.0004919201,0.0003244849,0.0001581062,0.0006126363,0.001068644,0.0003595968,0.000496043],"category_scores_gemma":[0.00001237562,0.000452694,0.00009583034,0.000586193,0.00002462805,0.0004908902,0.0002707881,0.0004095878,0.0000681757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001639804,"about_ca_system_score_gemma":0.00012192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001261091,"about_ca_topic_score_gemma":0.0003241729,"domain_scores_codex":[0.9978896,0.0001692973,0.0003537911,0.000825909,0.0002734314,0.0004879954],"domain_scores_gemma":[0.998871,0.0000651617,0.0003413893,0.0004432361,0.00011341,0.0001658093],"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":[0.0001450746,0.0002326145,0.003516998,0.004251403,0.0003872422,0.002047245,0.005821375,0.06622193,0.001256564,0.01948532,0.001026247,0.895608],"study_design_scores_gemma":[0.002811167,0.0002913893,0.001757156,0.002667294,0.0001101939,0.0001935924,0.005000708,0.7711552,0.0002478505,0.00009398995,0.2130588,0.002612586],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2961207,0.007023688,0.190528,0.00009698318,0.01767075,0.003262684,0.000100979,0.001325291,0.4838709],"genre_scores_gemma":[0.5851685,0.00006632577,0.00410331,0.00005322422,0.0005089977,0.000007629142,0.0002030886,0.00007596855,0.409813],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.8929954,"threshold_uncertainty_score":0.9997925,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01074419695119354,"score_gpt":0.1955828929431082,"score_spread":0.1848386959919147,"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."}}