{"id":"W2105768287","doi":"10.1109/dcc.2008.25","title":"List Update Algorithms for Data Compression","year":2008,"lang":"en","type":"article","venue":"DCC","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Algorithm; Data compression; Locality of reference; Compression (physics); Locality; Subroutine; Construct (python library); Compression ratio; Parallel computing; Cache; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00269472,0.001359491,0.00102937,0.004172389,0.00131602,0.003856717,0.002882983,0.001847131,0.01303574],"category_scores_gemma":[0.0165698,0.0005731769,0.000914027,0.00684359,0.001510582,0.006666237,0.002987157,0.002572321,0.008135371],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001678447,"about_ca_system_score_gemma":0.001571354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001631103,"about_ca_topic_score_gemma":0.00167942,"domain_scores_codex":[0.9958352,0.0007798952,0.0004662035,0.0004985775,0.002196775,0.0002233537],"domain_scores_gemma":[0.9908754,0.003023828,0.0005167507,0.003557509,0.001902315,0.0001242198],"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":[0.0002870625,0.0001147725,0.0008659986,0.0004990466,0.00006249626,0.00007743053,0.0002153726,0.0173503,0.006821578,0.1487134,0.0349052,0.7900872],"study_design_scores_gemma":[0.000210736,0.0003267112,0.001071695,0.0003319877,0.0001236268,0.001446656,0.0002377537,0.4235864,0.05659315,0.3297199,0.186194,0.0001574324],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004248885,0.003469491,0.9768627,0.0007201734,0.0003030495,0.0003542308,0.0006253398,0.005670264,0.007745927],"genre_scores_gemma":[0.06774476,0.002974746,0.9152485,0.0006149379,0.0005103181,0.00083818,0.002117391,0.001133854,0.0088173],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01303574,"threshold_uncertainty_score":0.0436089,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1267360926996233,"score_gpt":0.3246432032365211,"score_spread":0.1979071105368978,"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."}}