{"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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.003435114,0.0002811725,0.0007420039,0.0001571419,0.0002874142,0.001163993,0.002903183,0.0002871311,0.0004491127],"category_scores_gemma":[0.0001959553,0.0002169056,0.000287403,0.00009034331,0.0002653214,0.0003200213,0.003996252,0.0008470982,0.0005080309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001930229,"about_ca_system_score_gemma":0.0009844069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006289378,"about_ca_topic_score_gemma":0.00004298472,"domain_scores_codex":[0.9967852,0.0004570261,0.0004371222,0.0008047654,0.0009628047,0.0005530434],"domain_scores_gemma":[0.9969237,0.0002504979,0.0001769713,0.001871804,0.0004976135,0.0002793501],"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":[0.004393403,0.0004314924,0.0001372995,0.0009766733,0.0007605453,0.0002648519,0.006142476,0.3687938,0.0001702572,0.2616964,0.03007999,0.3261528],"study_design_scores_gemma":[0.02024816,0.00001126487,0.000009660453,0.00008389851,0.000006947562,0.000006552589,0.000004259977,0.9675972,0.0002474885,0.009928664,0.001573124,0.0002827416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00002684154,0.0001696066,0.8995736,0.002775134,0.0009016458,0.0008096865,0.000001562463,0.0003149601,0.09542701],"genre_scores_gemma":[0.4823172,0.0006698165,0.464594,0.00118464,0.0003717153,0.0002492217,0.00004219823,0.0000567965,0.05051443],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5988035,"threshold_uncertainty_score":0.9998729,"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."}}