{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009419401,0.00009239838,0.0001041553,0.00003906438,0.0001661245,0.0001638087,0.0003251716,0.00004450536,0.0001926101],"category_scores_gemma":[0.0004957374,0.00007490659,0.00002075258,0.0002180989,0.00007142655,0.005740998,0.0002712323,0.00009664096,0.00003106697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001299031,"about_ca_system_score_gemma":0.00003482341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002418854,"about_ca_topic_score_gemma":0.000002205814,"domain_scores_codex":[0.9991642,0.00004310345,0.0001783408,0.0002804928,0.0001773879,0.0001564708],"domain_scores_gemma":[0.9989421,0.0003457417,0.000139509,0.0003400427,0.0001709368,0.00006164024],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005353809,0.0006503753,0.01126833,0.0002136449,0.00009310754,0.00003226975,0.2343934,0.0001342087,0.05028265,0.08455782,0.01334963,0.604971],"study_design_scores_gemma":[0.002555855,0.001428869,0.02549001,0.0007436132,0.00008927903,0.0007347002,0.06568194,0.08565506,0.1091863,0.03845217,0.6668377,0.003144526],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05378804,0.0003898899,0.9442469,0.0004369846,0.0003013934,0.0001154197,0.000002372346,0.00007772211,0.0006412659],"genre_scores_gemma":[0.6088639,0.0001773437,0.3903575,0.0004542365,0.00003457112,0.00003410336,0.000003396886,0.000005604189,0.00006930596],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6534881,"threshold_uncertainty_score":0.4162085,"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."}}