{"id":"W2733630045","doi":"10.1109/ipdps.2017.52","title":"Tight Load Balancing Via Randomized Local Search","year":2017,"lang":"en","type":"preprint","venue":"","topic":"Optimization and Search Problems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; University of British Columbia; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Load balancing (electrical power); Bin; Ball (mathematics); Randomized algorithm; Queueing theory; Mathematics; Combinatorics; Computer science; Discrete mathematics; Algorithm; Statistics; Geometry","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":[0.002587225,0.001992749,0.003047024,0.001146027,0.001230214,0.002663204,0.004917569,0.00235345,0.008885567],"category_scores_gemma":[0.01264946,0.001132577,0.0009749208,0.001833134,0.001890133,0.004277349,0.003408998,0.002317592,0.002169231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002261919,"about_ca_system_score_gemma":0.002397688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004630323,"about_ca_topic_score_gemma":0.005604288,"domain_scores_codex":[0.9969028,0.001057341,0.0001049462,0.0007669896,0.0005595787,0.0006082885],"domain_scores_gemma":[0.9935214,0.004172768,0.0007544128,0.0007425551,0.0003776205,0.000431312],"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":[0.0005660162,0.000259044,0.0004361185,0.0002007483,0.00008666814,0.0001320938,0.00009897038,0.9291009,0.001932666,0.03081056,0.006533491,0.02984264],"study_design_scores_gemma":[0.0000601584,0.00003484063,0.00003196805,0.000006242162,0.00001068811,0.00001437477,0.00001109149,0.9870614,0.0002134553,0.0120698,0.0004793665,0.000006612192],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0364806,0.00103591,0.9483008,0.0009628783,0.0001597641,0.0002258304,0.000248426,0.003302987,0.009282785],"genre_scores_gemma":[0.8090522,0.0005053018,0.1801312,0.0007243438,0.0002195288,0.0004916818,0.0005841675,0.0005436054,0.007747935],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008885567,"threshold_uncertainty_score":0.02972519,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02664937889080082,"score_gpt":0.297971945999993,"score_spread":0.2713225671091922,"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."}}