{"id":"W2049349451","doi":"10.1109/dcc.2010.28","title":"Lossless Data Compression via Substring Enumeration","year":2010,"lang":"en","type":"article","venue":"","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Substring; Lossless compression; Lexicographical order; Compression (physics); Data compression; Enumeration; Algorithm; String (physics); Computer science; Mathematics; Compression ratio; Set (abstract data type); Combinatorics; 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":[0.0003926969,0.0005676741,0.0006347023,0.001607938,0.0004252855,0.0007014665,0.001058817,0.000581778,0.002408814],"category_scores_gemma":[0.002492051,0.0002131782,0.0002777999,0.00253284,0.0006164993,0.002257509,0.001008602,0.0006978263,0.001410332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002787465,"about_ca_system_score_gemma":0.000382258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005012891,"about_ca_topic_score_gemma":0.0005971179,"domain_scores_codex":[0.9993949,0.00009650495,0.00004310461,0.00007334488,0.0003425368,0.00004956869],"domain_scores_gemma":[0.9987713,0.0005058189,0.00009862665,0.0004140796,0.0001828779,0.00002732562],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000548825,0.0001353656,0.0007511209,0.0003129803,0.00004639297,0.0003257795,0.0001395115,0.02876884,0.08097194,0.02037429,0.007695593,0.8599293],"study_design_scores_gemma":[0.0001384613,0.0005276738,0.001215579,0.0001123751,0.00008192143,0.002379548,0.00013095,0.5989627,0.3077933,0.05674716,0.03184218,0.00006813842],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03855192,0.001908292,0.9528964,0.0003431432,0.0001408421,0.00008676673,0.0003541138,0.002855446,0.002863167],"genre_scores_gemma":[0.3053871,0.001926788,0.681399,0.0003506386,0.0002244832,0.0002087114,0.001418404,0.0002516467,0.008833183],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002408814,"threshold_uncertainty_score":0.00805825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02874988235034151,"score_gpt":0.2778908061616329,"score_spread":0.2491409238112914,"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."}}