{"id":"W2151954927","doi":"10.1109/hipc.1997.634496","title":"A hierarchical processor scheduling policy for distributed-memory multicomputer systems","year":2002,"lang":"en","type":"article","venue":"","topic":"Distributed and Parallel Computing Systems","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Partition (number theory); Scheduling (production processes); Distributed computing; Time-sharing; Parallel computing; Processor scheduling; Operating system; Schedule; Mathematical optimization","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001324598,0.0004320107,0.0003940702,0.0004692886,0.001074586,0.0008621715,0.00110915,0.000584422,0.002221562],"category_scores_gemma":[0.003494602,0.0003024871,0.0002488979,0.0006860374,0.0005655424,0.001179369,0.0008383908,0.0007513787,0.0008016573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001335435,"about_ca_system_score_gemma":0.002304852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003610807,"about_ca_topic_score_gemma":0.004586427,"domain_scores_codex":[0.9990001,0.000287702,0.00007893513,0.0001276873,0.0003681902,0.0001372503],"domain_scores_gemma":[0.9986607,0.000350722,0.0001509272,0.0003210863,0.0003203769,0.0001961242],"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":[0.0005924082,0.0003969994,0.003019636,0.0003397169,0.00007319415,0.0003080558,0.0005010188,0.5484342,0.03846128,0.1399627,0.02581605,0.2420948],"study_design_scores_gemma":[0.00007124383,0.0001162064,0.0007004439,0.00001552614,0.00002058834,0.0001044543,0.00005361616,0.9595332,0.007698519,0.01827129,0.01337835,0.0000365507],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06623054,0.0007163972,0.9188207,0.001016155,0.0002311439,0.0003260893,0.0001885542,0.002501454,0.009968924],"genre_scores_gemma":[0.6565428,0.0004480451,0.3354114,0.0002768023,0.000161474,0.0002425691,0.0002590291,0.0001954917,0.006462348],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003610807,"threshold_uncertainty_score":0.009689331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02813952596271976,"score_gpt":0.2631703731397961,"score_spread":0.2350308471770764,"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."}}