{"id":"W1976682045","doi":"10.1145/1242471.1242472","title":"A taxonomy of suffix array construction algorithms","year":2014,"lang":"en","type":"review","venue":"Minerva Access (University of Melbourne)","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":307,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Suffix array; Suffix; Algorithm; Implementation; Compressed suffix array; Suffix tree; Generalized suffix tree; Data structure; Theoretical computer science; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003797966,0.0003604875,0.001735713,0.0005018602,0.0001768271,0.00008651624,0.003956224,0.0003139968,0.0001076591],"category_scores_gemma":[0.00002506765,0.0003540583,0.0005361752,0.0007720087,0.0002618825,0.001395292,0.001280806,0.0003215756,0.00002490405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006860647,"about_ca_system_score_gemma":0.0003176693,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005915336,"about_ca_topic_score_gemma":0.00002926637,"domain_scores_codex":[0.9979572,0.0002172149,0.0004796963,0.0006296829,0.0004288833,0.000287357],"domain_scores_gemma":[0.9968958,0.0001710053,0.001435513,0.001089134,0.0002538369,0.0001547077],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000002648093,0.00005538295,0.000008688434,0.003019175,0.00009221764,0.0000113968,0.00006404397,0.000003456053,0.000002345035,0.001246236,0.001548569,0.9939458],"study_design_scores_gemma":[0.0003303484,0.00005718248,0.00001770463,0.00411896,0.0002304161,0.00005990859,0.00005703968,0.00175315,0.00001046115,0.0001199902,0.9928812,0.0003635715],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.000008042973,0.3895606,0.6085314,0.00003305568,0.0004242393,0.0003217353,0.00005051969,0.00004678317,0.001023647],"genre_scores_gemma":[0.00001241498,0.8195707,0.1796879,0.000007737845,0.000134134,0.000001975777,0.00006954404,0.00001660707,0.0004989099],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.9935822,"threshold_uncertainty_score":0.9998912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07356066266408752,"score_gpt":0.287019720484159,"score_spread":0.2134590578200715,"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."}}