{"id":"W2151705548","doi":"10.12785/isl/010106","title":"Performance Assessment of Feasible Scheduling Multiprocessor Tasks Solutions by using DEA FDH method","year":2012,"lang":"en","type":"article","venue":"Information Sciences Letters","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Multiprocessing; Parallel computing; Computer science; Scheduling (production processes); Multiprocessor scheduling; Mathematical optimization; Dynamic priority scheduling; Operating system; Mathematics; Two-level scheduling; Schedule","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":[0.002350354,0.0004686813,0.0005094983,0.001046372,0.0003521933,0.0005547651,0.0004732862,0.0006576831,0.001739143],"category_scores_gemma":[0.00634501,0.0001679479,0.0004099149,0.0007028881,0.0003248862,0.0006417671,0.0003415396,0.0004530349,0.0001548221],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000580063,"about_ca_system_score_gemma":0.0005892602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002563085,"about_ca_topic_score_gemma":0.001611461,"domain_scores_codex":[0.999136,0.0004532704,0.00003838524,0.00005677106,0.0002407941,0.00007477916],"domain_scores_gemma":[0.9965293,0.002805569,0.0001291592,0.0001599966,0.0003246644,0.00005142005],"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":[0.0003748892,0.0001285769,0.001317936,0.0001571473,0.00005038625,0.00006564715,0.00007024565,0.8810386,0.005569683,0.01085187,0.0005165139,0.09985852],"study_design_scores_gemma":[0.00001125087,0.00006997732,0.0002463925,0.000004726598,0.000003749545,0.00001256629,0.00001594772,0.9969202,0.001468799,0.0009816851,0.000259686,0.000005023843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.18792,0.0006859692,0.80072,0.000213431,0.00008067006,0.0001320791,0.000126438,0.0002990585,0.009822401],"genre_scores_gemma":[0.7227818,0.0002115403,0.2757897,0.00002469839,0.00001409796,0.0001295611,0.0001106473,0.00004173815,0.000896115],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002563085,"threshold_uncertainty_score":0.01243001,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04219280169622666,"score_gpt":0.3170622354368769,"score_spread":0.2748694337406502,"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."}}