{"id":"W3017834538","doi":"10.1016/j.tcs.2020.04.009","title":"A linear-space data structure for range-LCP queries in poly-logarithmic time","year":2020,"lang":"en","type":"article","venue":"Theoretical Computer Science","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Ministry of Science and Technology, Taiwan; National Science Foundation","keywords":"Linear space; Data structure; Combinatorics; Mathematics; Binary logarithm; Space (punctuation); Logarithm; Log-log plot; Range query (database); Range (aeronautics); Suffix; Time complexity; Inverse; Discrete mathematics; Computer science; Search engine","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":[0.002019712,0.001566429,0.002509184,0.0035291,0.001861367,0.004467282,0.003395681,0.002221719,0.02835851],"category_scores_gemma":[0.01053,0.001114888,0.001659405,0.01043896,0.002053944,0.01116056,0.007491876,0.00361545,0.009491463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003783696,"about_ca_system_score_gemma":0.004702066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003747398,"about_ca_topic_score_gemma":0.004466397,"domain_scores_codex":[0.9949114,0.0006741263,0.0005924583,0.0009205765,0.002223485,0.0006780839],"domain_scores_gemma":[0.9875886,0.003313798,0.0005776928,0.00684333,0.001199493,0.0004770469],"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":[0.003079851,0.001059196,0.002505766,0.001041999,0.0001831758,0.0002427452,0.0006569671,0.02518052,0.04321457,0.1067905,0.1321077,0.683937],"study_design_scores_gemma":[0.002196852,0.001267858,0.002038329,0.0003012939,0.0003713623,0.001165498,0.0009400552,0.3996903,0.07098174,0.4280676,0.09264484,0.0003341663],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04777906,0.001919278,0.8850842,0.00331623,0.0005904362,0.001074486,0.007328314,0.03553616,0.01737182],"genre_scores_gemma":[0.2812899,0.000568928,0.6838547,0.001689672,0.0004832734,0.001533265,0.01191226,0.003081948,0.01558601],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02835851,"threshold_uncertainty_score":0.09486872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02061047038952822,"score_gpt":0.2714613027795172,"score_spread":0.250850832389989,"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."}}