{"id":"W2070695081","doi":"10.1016/j.apm.2014.09.004","title":"Online dispatching and parallel processing algorithms for saving money in systems with heterogeneous, single-buffered, speed-scalable processors","year":2014,"lang":"en","type":"article","venue":"Applied Mathematical Modelling","topic":"Distributed and Parallel Computing Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Scalability; Overhead (engineering); Task (project management); Speedup; Computation; Parallel computing; Software; Distributed computing; Real-time computing; Algorithm; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007248911,0.0003629523,0.0006388465,0.0001160515,0.0002204545,0.0005902439,0.0005394602,0.0001326898,4.026675e-7],"category_scores_gemma":[0.00002696746,0.0002919034,0.0000465544,0.0002992782,0.00005461062,0.0002558141,0.0001491594,0.0002228629,0.000003842297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005559909,"about_ca_system_score_gemma":0.0000389517,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002179491,"about_ca_topic_score_gemma":0.000004649125,"domain_scores_codex":[0.9974727,0.00004566662,0.0006693351,0.0007662083,0.000385227,0.0006608766],"domain_scores_gemma":[0.9987134,0.000382398,0.0002441336,0.0003883241,0.00008870751,0.0001829927],"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":[0.00002552901,0.0002714046,0.00003118296,0.001377346,0.00001793137,0.000003815644,0.001129206,0.946734,0.000120446,0.04519282,0.000003447499,0.005092846],"study_design_scores_gemma":[0.0007604233,0.00009000529,0.000002971325,0.0008978829,0.00001680277,0.00005297869,0.0001133008,0.9682425,0.0000555757,0.02931204,0.00006656321,0.0003889313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08043149,0.0002114481,0.9177588,0.00007682787,0.00004425312,0.0008344267,0.000004719683,0.0002301271,0.0004078841],"genre_scores_gemma":[0.7109401,0.000002865205,0.2888021,0.00003115635,0.00008474775,0.00006701557,0.00001190899,0.00003484175,0.00002523668],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6305087,"threshold_uncertainty_score":0.9999533,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03562597815282596,"score_gpt":0.2435858324483094,"score_spread":0.2079598542954834,"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."}}