{"id":"W1791987072","doi":"10.1002/spe.2203","title":"Decoding billions of integers per second through vectorization","year":2013,"lang":"en","type":"article","venue":"Software Practice and Experience","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":227,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université TÉLUQ","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Decoding methods; Vectorization (mathematics); Scheme (mathematics); Encoding (memory); Data compression; Compression (physics)","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":[0.0006007591,0.001046308,0.0004706507,0.001334714,0.0003849333,0.001129654,0.0009398051,0.0004814113,0.006816084],"category_scores_gemma":[0.002896723,0.0003201948,0.0003306268,0.001914738,0.0005864769,0.001870518,0.001360931,0.0007055185,0.003715346],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004706003,"about_ca_system_score_gemma":0.0008847202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001403181,"about_ca_topic_score_gemma":0.001511983,"domain_scores_codex":[0.9992919,0.0000953589,0.00008126787,0.0000946104,0.0003772395,0.0000596152],"domain_scores_gemma":[0.9991148,0.0002381349,0.00007586155,0.0002796312,0.0002696565,0.00002194504],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005567013,0.00007000194,0.0007115474,0.000225372,0.00003371997,0.0001437626,0.0002705276,0.01228167,0.06764778,0.02612046,0.01291351,0.8790249],"study_design_scores_gemma":[0.0002391166,0.0006060284,0.001280927,0.0001703044,0.00006834355,0.001041934,0.0003386968,0.4025892,0.369895,0.05301471,0.1706289,0.0001268719],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04711363,0.001695903,0.9232878,0.0005004415,0.000362676,0.0002379759,0.0005461726,0.01506042,0.01119499],"genre_scores_gemma":[0.2423444,0.001474154,0.7368845,0.000305047,0.0001449936,0.0003629501,0.002423597,0.001079748,0.01498066],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006816084,"threshold_uncertainty_score":0.02280211,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01574918514338506,"score_gpt":0.2803758415423402,"score_spread":0.2646266563989551,"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."}}