{"id":"W4386204710","doi":"10.1145/3603719.3603731","title":"LearnedSort as a learning-augmented SampleSort: Analysis and Parallelization","year":2023,"lang":"en","type":"article","venue":"","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Sorting; Parallel computing; Sorting algorithm; Artificial intelligence; Machine learning; Algorithm","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002536717,0.001376359,0.001116123,0.001348274,0.0008250425,0.003225563,0.003023261,0.0009428603,0.007604879],"category_scores_gemma":[0.0146054,0.0005788713,0.0011332,0.002591535,0.001592625,0.005876588,0.002004541,0.002338989,0.002560416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002469443,"about_ca_system_score_gemma":0.005567438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005208333,"about_ca_topic_score_gemma":0.007927111,"domain_scores_codex":[0.9968247,0.0005462297,0.0002338945,0.0005704954,0.001497994,0.0003266536],"domain_scores_gemma":[0.9935489,0.00240087,0.0003305925,0.002301325,0.001199628,0.0002186943],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009921992,0.000350429,0.003772775,0.0003801838,0.0001376556,0.0002095245,0.0002914431,0.2811013,0.007957189,0.1208319,0.0300399,0.5539355],"study_design_scores_gemma":[0.00009848193,0.00009992172,0.0002597309,0.00003057862,0.00002832006,0.0001242167,0.00006324984,0.9270291,0.01141381,0.05012392,0.01070524,0.00002356263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03765986,0.0007577105,0.9308705,0.000795663,0.0003788045,0.0002361069,0.0005192676,0.01969096,0.00909109],"genre_scores_gemma":[0.2652463,0.000515474,0.7213883,0.0005246992,0.000226088,0.0003922656,0.001743035,0.003332427,0.006631277],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007604879,"threshold_uncertainty_score":0.02544081,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01416346119453075,"score_gpt":0.2667579189090319,"score_spread":0.2525944577145012,"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."}}