{"id":"W2009268872","doi":"10.1109/dcc.2014.68","title":"Improving Compression via Substring Enumeration by Explicit Phase Awareness","year":2014,"lang":"en","type":"article","venue":"","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Lossless compression; Substring; Computer science; Compression (physics); Data compression; Byte; Algorithm; Synchronization (alternating current); Enumeration; Code (set theory); Universal code; Phase (matter); Compression ratio; Theoretical computer science; Mathematics; Data structure; Decoding methods; Block code; Discrete mathematics; Programming language; Linear code; Telecommunications","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":[0.0006283946,0.0005901047,0.0004851708,0.001345833,0.000349669,0.0006963767,0.0008115461,0.0006414723,0.001413971],"category_scores_gemma":[0.004262974,0.0002766876,0.0003142953,0.00170378,0.000660073,0.002392782,0.001207922,0.0008748744,0.0005065575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003051685,"about_ca_system_score_gemma":0.0005651957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006963836,"about_ca_topic_score_gemma":0.00103142,"domain_scores_codex":[0.9993599,0.0001029483,0.00005059106,0.00007514196,0.0003595484,0.00005188458],"domain_scores_gemma":[0.9973254,0.001393235,0.0002184371,0.0007008984,0.0003129942,0.00004892982],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003254895,0.0001700789,0.001652693,0.0001654976,0.00003505585,0.000156455,0.0002181525,0.05357314,0.1544028,0.0231211,0.001858602,0.7643209],"study_design_scores_gemma":[0.00006309321,0.0003332843,0.001340009,0.00003733104,0.00004174448,0.0007327647,0.0001021698,0.6587729,0.3135375,0.01596438,0.009022021,0.00005268996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06299341,0.0005348233,0.9321968,0.0001847914,0.00004778443,0.0000664625,0.00004945007,0.001855673,0.002070826],"genre_scores_gemma":[0.4153783,0.0006003077,0.579571,0.0001799755,0.00008124595,0.00007091211,0.0003004766,0.0002336207,0.003584126],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001413971,"threshold_uncertainty_score":0.004730225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0118491525656889,"score_gpt":0.2620469084618924,"score_spread":0.2501977558962035,"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."}}