{"id":"W2073984053","doi":"10.1016/j.is.2010.11.001","title":"Suffix trees for inputs larger than main memory","year":2010,"lang":"en","type":"article","venue":"Information Systems","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Generalized suffix tree; Compressed suffix array; Suffix tree; Suffix; Computer science; String (physics); String searching algorithm; Auxiliary memory; Algorithm; Data structure; Tree (set theory); Theoretical computer science; Construct (python library); Mathematics; Combinatorics; Programming language","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":[0.000522704,0.00009340697,0.0001168924,0.0001133299,0.0001468081,0.0004724083,0.0005499934,0.00008174511,0.000006577681],"category_scores_gemma":[0.0000426964,0.00007233633,0.00004383855,0.0001334295,0.00001371346,0.003369526,0.0001257796,0.00009828924,0.0001831046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001440007,"about_ca_system_score_gemma":0.00005225377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006046483,"about_ca_topic_score_gemma":0.00001383214,"domain_scores_codex":[0.9991078,0.0000201405,0.0003331493,0.0001056887,0.0002504816,0.000182784],"domain_scores_gemma":[0.9990758,0.00006655017,0.0001714061,0.0004868234,0.0001253538,0.00007408697],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005297985,0.0001608461,0.00143991,0.0005388308,0.00007715009,0.000006064519,0.01576454,0.002032454,0.008189701,0.4679263,0.2776964,0.2261148],"study_design_scores_gemma":[0.0005124853,0.00003764013,0.001221134,0.00003084345,0.000002092065,0.00003320888,0.0001394318,0.3827564,0.0009959678,0.0001833797,0.6139048,0.0001825719],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009201319,0.00001909196,0.9827647,0.0002455945,0.003179632,0.000438393,0.00007316082,0.0001912557,0.003886893],"genre_scores_gemma":[0.9813427,0.000002128347,0.01723135,0.0003221008,0.0003428839,0.0001249843,0.000110569,0.000005910185,0.000517343],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9721414,"threshold_uncertainty_score":0.4555444,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01017456959835393,"score_gpt":0.2344620922604449,"score_spread":0.224287522662091,"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."}}