{"id":"W2549141685","doi":"10.1016/j.is.2017.01.002","title":"Upscaledb: Efficient integer-key compression in a key-value store using SIMD instructions","year":2017,"lang":"en","type":"article","venue":"Information Systems","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université TÉLUQ","funders":"","keywords":"Computer science; SIMD; Byte; Data compression; Key (lock); Associative array; Parallel computing; Compression (physics); Compression ratio; Database; Operating system; Algorithm; Programming language","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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0004685569,0.0001595662,0.0002243225,0.0002838356,0.0007159567,0.001239471,0.001125915,0.0001115267,0.000003011108],"category_scores_gemma":[0.00007560633,0.0001328808,0.00005040714,0.0001804934,0.00005573003,0.004410977,0.0006376918,0.0002028937,0.00009189754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001676445,"about_ca_system_score_gemma":0.00008730156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001172745,"about_ca_topic_score_gemma":0.00001190179,"domain_scores_codex":[0.9983696,0.00007836588,0.0006213327,0.0001976551,0.0004811343,0.0002518726],"domain_scores_gemma":[0.9979994,0.00003633029,0.0005679476,0.001128756,0.0001623632,0.0001051799],"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":[0.00004792132,0.000198721,0.006317354,0.0003822171,0.0000364072,0.00001686679,0.01902263,0.755627,0.0008453743,0.1346837,0.003769106,0.07905264],"study_design_scores_gemma":[0.0005116615,0.00001903634,0.00381918,0.0003955771,0.000002154104,0.00004762936,0.0003530925,0.9707604,0.00007779621,0.00004947537,0.02379719,0.0001668101],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1346029,0.00005564319,0.8561882,0.00007747059,0.003485791,0.0004232225,0.000026135,0.0001362212,0.005004411],"genre_scores_gemma":[0.9909252,0.000004175608,0.008853837,0.00003927413,0.0001043689,0.00002137701,0.0000192785,0.000005474914,0.00002703397],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8563223,"threshold_uncertainty_score":0.9997973,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02574336979932961,"score_gpt":0.2814763698162006,"score_spread":0.2557330000168709,"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."}}