{"id":"W2264090060","doi":"10.1007/978-3-662-48350-3_74","title":"Compressed Data Structures for Dynamic Sequences","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Substring; Symbol (formal); Computer science; String (physics); Data structure; Alphabet; Algorithm; Combinatorics; Representation (politics); Lossless compression; Entropy (arrow of time); Data compression; Mathematics; Physics","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":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.001180689,0.0006392663,0.000683182,0.0005770427,0.0003604288,0.0009507136,0.01294703,0.0003617451,0.00001803378],"category_scores_gemma":[0.0001604862,0.000523991,0.00009204691,0.0003978884,0.0007518306,0.001672986,0.006248621,0.0006515447,0.0000173793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002477257,"about_ca_system_score_gemma":0.001107816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004624805,"about_ca_topic_score_gemma":0.0001005689,"domain_scores_codex":[0.9947433,0.00003665725,0.0005919494,0.002553015,0.001313812,0.0007611982],"domain_scores_gemma":[0.9939285,0.0005764703,0.0003991569,0.004378147,0.0004424013,0.0002753185],"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":[0.00001446324,0.0000250723,0.00000751045,0.00007183703,0.00002065668,0.00005353903,0.000244231,0.0279148,0.0001395005,0.02742184,0.001098139,0.9429884],"study_design_scores_gemma":[0.0002673922,0.0001075325,0.00002183404,0.0001627522,0.000008234731,0.00004030029,1.219859e-7,0.6696839,0.00009470656,0.3151703,0.01395462,0.0004882622],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000009768268,0.001985592,0.9926678,0.0005783382,0.002900867,0.0006741687,0.0002952538,0.0002102227,0.0006780248],"genre_scores_gemma":[0.01790214,0.00006697383,0.9800379,0.0006703634,0.0005964779,0.00001448662,0.0003497138,0.00004872361,0.0003132827],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9425002,"threshold_uncertainty_score":0.9997212,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05750524399227357,"score_gpt":0.3123359668703994,"score_spread":0.2548307228781258,"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."}}