{"id":"W2974083459","doi":"10.1002/spe.960","title":"A survey of practical algorithms for suffix tree construction in external memory","year":2010,"lang":"en","type":"article","venue":"Software Practice and Experience","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Suffix; Suffix tree; Computer science; Auxiliary memory; Scalability; Generalized suffix tree; Trie; Algorithm; Tree (set theory); Theoretical computer science; Data structure; Mathematics; Programming language; Database; Operating system","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":[],"consensus_categories":[],"category_scores_codex":[0.0008939721,0.000123777,0.000185296,0.00008049595,0.0001090286,0.0001213422,0.0003459913,0.00009730403,0.00001043198],"category_scores_gemma":[0.004481739,0.0001107414,0.00002591642,0.0002609646,0.0001837019,0.002734543,0.0002443425,0.0002801152,0.000002287971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001028062,"about_ca_system_score_gemma":0.0001517033,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009690801,"about_ca_topic_score_gemma":0.0001895573,"domain_scores_codex":[0.9986665,0.0001156353,0.0003003122,0.0004250065,0.0002615464,0.0002309551],"domain_scores_gemma":[0.9971451,0.001830692,0.0002316813,0.0004256704,0.0002633157,0.0001035521],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003400828,0.0005345179,0.02120383,0.0000509603,0.00001506824,0.00005977753,0.008160448,0.000008135557,0.005809449,0.01191517,0.0003295981,0.951573],"study_design_scores_gemma":[0.01019698,0.002622124,0.5381816,0.0004824153,0.00009139533,0.00479269,0.01326201,0.3279763,0.0451543,0.01605033,0.03807465,0.003115227],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1116976,0.0001753433,0.8868242,0.0002812772,0.0006891752,0.0002028527,0.00001511972,0.00004655083,0.00006792646],"genre_scores_gemma":[0.1962549,0.00004901866,0.8034703,0.0001031969,0.00005044994,0.00004724458,0.000005472527,0.000006283576,0.00001318656],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9484577,"threshold_uncertainty_score":0.5365384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03567803922419386,"score_gpt":0.3446788086252816,"score_spread":0.3090007694010877,"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."}}