{"id":"W4246170086","doi":"10.1504/ijwgs.2008.018498","title":"Parallel lossless data compression using the Burrows-Wheeler Transform","year":2008,"lang":"en","type":"article","venue":"International Journal of Web and Grid Services","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Argonne National Laboratory","keywords":"Computer science; Parallel computing; Lossless compression; Speedup; Task parallelism; Distributed memory; Data compression; Parallelism (grammar); Algorithm; Shared memory","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":[],"consensus_categories":[],"category_scores_codex":[0.0003070838,0.00009096812,0.000125406,0.0001004566,0.0001666714,0.0001549442,0.002315467,0.00003521082,0.000006622113],"category_scores_gemma":[0.000005816267,0.00005759829,0.00004167813,0.00008937714,0.00004903457,0.0008991767,0.0003391528,0.0001522416,0.000001048417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001426373,"about_ca_system_score_gemma":0.00006948584,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003711521,"about_ca_topic_score_gemma":0.000008598162,"domain_scores_codex":[0.9989612,0.0000492439,0.0003166388,0.0001437022,0.0004294745,0.00009979966],"domain_scores_gemma":[0.9990637,0.00006634126,0.0002669022,0.0002474044,0.0003030203,0.00005261358],"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":[0.001586327,0.002687864,0.1000433,0.0004844833,0.003394062,0.002861983,0.0460676,0.4943241,0.0175582,0.03677616,0.07364462,0.2205712],"study_design_scores_gemma":[0.0006484647,0.00004759595,0.002286727,0.0001593092,0.00001178962,0.001354883,0.00007534483,0.9746166,0.0005194814,0.00109654,0.01904902,0.0001341663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.198382,0.001641694,0.7921699,0.005534571,0.001496902,0.00008023222,0.00001531538,0.00005875998,0.0006206941],"genre_scores_gemma":[0.9189867,0.001381325,0.07827899,0.0007624105,0.0005422577,4.44284e-7,0.000007253299,0.000005613958,0.00003502333],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7206047,"threshold_uncertainty_score":0.4302752,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0467440099253441,"score_gpt":0.3089810611127415,"score_spread":0.2622370511873974,"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."}}