{"id":"W2543444511","doi":"10.1109/icm.2010.5696192","title":"An efficient scheduling methodology for heterogeneous multi-core processor systems","year":2010,"lang":"en","type":"article","venue":"","topic":"Distributed and Parallel Computing Systems","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Scheduling (production processes); Directed acyclic graph; Multiprocessing; Schedule; Distributed computing; Greedy algorithm; Parallel computing; Multiprocessor scheduling; Heuristic; Processor scheduling; Dynamic priority scheduling; Multi-core processor; Symmetric multiprocessor system; Two-level scheduling; Mathematical optimization; Algorithm; Operating system; Artificial intelligence","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":[0.001379133,0.0001971425,0.0003029637,0.00009160086,0.0002212476,0.0003160536,0.00116137,0.0001608264,0.000002961524],"category_scores_gemma":[0.0001505404,0.0001606618,0.00009332151,0.0002196191,0.00003992107,0.0001105023,0.0001093627,0.0001807168,0.00002799886],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001537621,"about_ca_system_score_gemma":0.0000865788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006014566,"about_ca_topic_score_gemma":0.00002322347,"domain_scores_codex":[0.9982113,0.0001238999,0.0003755355,0.0006296522,0.0001845623,0.000474999],"domain_scores_gemma":[0.9983378,0.0003049707,0.0001590019,0.0007421632,0.0002683853,0.0001876492],"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":[0.00001575491,0.0003075555,0.0002970427,0.0001846662,0.00004044846,0.00001206756,0.0008203238,0.9020898,0.02179625,0.07123656,0.00008461198,0.003114925],"study_design_scores_gemma":[0.0004609385,0.0001293663,0.00004847493,0.00001575886,0.000005543469,0.0001379617,0.00003870963,0.9953589,0.001639386,0.0001419784,0.001795323,0.0002276631],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.174897,0.00007334838,0.8214422,0.00005788908,0.002486201,0.0005072923,0.00000797448,0.0004549578,0.00007314479],"genre_scores_gemma":[0.6378576,1.76766e-7,0.3617474,0.0000540677,0.0001731005,0.00005785577,0.000008620535,0.00001057768,0.00009058037],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4629606,"threshold_uncertainty_score":0.6551598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1098911703688039,"score_gpt":0.3564260492005476,"score_spread":0.2465348788317437,"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."}}