{"id":"W4225807748","doi":"10.48550/arxiv.2111.07244","title":"A Simple Approximation Algorithm for Vector Scheduling and Applications to Stochastic Min-Norm Load Balancing","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Job shop scheduling; Combinatorics; Approximation algorithm; Mathematics; Binary logarithm; Scheduling (production processes); Norm (philosophy); Upper and lower bounds; Monotone polygon; Sigma; Random variable; Discrete mathematics; Algorithm; Mathematical optimization; Computer science; Statistics; Physics; Schedule; Mathematical analysis","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000148079,0.0002727661,0.0002989252,0.0001486792,0.0001511003,0.0001282175,0.0002132086,0.000232857,0.00001845045],"category_scores_gemma":[0.00005774826,0.0003696368,0.0001009392,0.0004053028,0.00002637814,0.0001190695,0.0001944469,0.0002807737,0.00001111708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003116398,"about_ca_system_score_gemma":0.0001149034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002439437,"about_ca_topic_score_gemma":0.00002254042,"domain_scores_codex":[0.9988133,0.00001798961,0.0002050654,0.0006072037,0.00007627236,0.0002801905],"domain_scores_gemma":[0.9989644,0.0001117897,0.00007322845,0.0003879734,0.0002671917,0.0001954614],"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":[0.00000458119,0.00001951725,0.00001666916,0.0001680834,0.00007053866,0.000003698604,0.000207341,0.9924873,0.00005774438,0.0006339864,0.00001124732,0.006319333],"study_design_scores_gemma":[0.0003943558,0.00001435004,0.00005438194,0.00009324057,0.0001089858,0.000002594524,0.0005028757,0.9975458,0.0001562704,0.0006598586,0.00008397028,0.0003832765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0322546,0.0002315305,0.9658903,0.00001714569,0.0002578203,0.0008270619,0.00007888262,0.0003550761,0.00008761023],"genre_scores_gemma":[0.7419701,0.0000595915,0.2571899,0.00003688483,0.0002504767,0.00005104448,0.0002560505,0.00006322806,0.0001226997],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7097155,"threshold_uncertainty_score":0.9998755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0303294378637732,"score_gpt":0.1942515859724649,"score_spread":0.1639221481086917,"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."}}