{"id":"W2070641109","doi":"10.1145/1777432.1777433","title":"Dynamic lightweight text compression","year":2010,"lang":"en","type":"article","venue":"ACM Transactions on Information Systems","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Agencia Española de Cooperación Internacional para el Desarrollo; Fondo Nacional de Desarrollo Científico y Tecnológico; Xunta de Galicia; Mountain Equipment Co-operative","keywords":"Computer science; Uncompressed video; Communication source; Novelty; Lossless compression; Gas compressor; Compression (physics); Compression ratio; Natural language; Data compression; Semantic compression; Artificial intelligence; Telecommunications; Video processing","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005862818,0.0005477152,0.0005777635,0.001378943,0.0006375411,0.001072393,0.001421543,0.0008015357,0.00418742],"category_scores_gemma":[0.004413724,0.000223176,0.0003369053,0.001540902,0.00101847,0.002822106,0.001790557,0.001009595,0.002038761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003944132,"about_ca_system_score_gemma":0.0006158184,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003908892,"about_ca_topic_score_gemma":0.0005486766,"domain_scores_codex":[0.9991246,0.0001110815,0.0000525366,0.0001620026,0.0004802328,0.00006962611],"domain_scores_gemma":[0.9969445,0.001155101,0.0002736619,0.001138779,0.0004064871,0.0000814443],"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":[0.0004215559,0.0001618644,0.0008267781,0.0004850917,0.00004406395,0.0005333702,0.0003662134,0.01865732,0.162204,0.0476983,0.009539738,0.7590617],"study_design_scores_gemma":[0.0001449407,0.0005299193,0.002193959,0.0001740911,0.0001206582,0.004407522,0.0003688065,0.493848,0.3462791,0.07996229,0.07186128,0.0001093874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03636772,0.001429092,0.951376,0.0007183749,0.0002458776,0.0001849973,0.000248173,0.002043593,0.007386115],"genre_scores_gemma":[0.4522831,0.002160055,0.5249498,0.0006779355,0.0008545179,0.0003332943,0.0007911618,0.0004129835,0.01753715],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00418742,"threshold_uncertainty_score":0.01400834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008293689010632505,"score_gpt":0.2360176442566523,"score_spread":0.2277239552460197,"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."}}