{"id":"W1596809600","doi":"","title":"Suffix arrays: what are they good for?","year":2006,"lang":"en","type":"article","venue":"Murdoch Research Repository (Murdoch University)","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Suffix; Compressed suffix array; Computer science; Generalized suffix tree; Suffix array; Suffix tree; Theoretical computer science; Algorithm; Linguistics","routes":{"ca_aff":true,"ca_fund":true,"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","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0008824012,0.0003021898,0.0003366672,0.0008362574,0.001674238,0.001442838,0.002930921,0.0002129994,0.00001067905],"category_scores_gemma":[0.00005903934,0.000298404,0.0002185916,0.001209432,0.0002311571,0.003793456,0.001279098,0.0005940563,0.00006193596],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000448797,"about_ca_system_score_gemma":0.0003475079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001045362,"about_ca_topic_score_gemma":0.0001442961,"domain_scores_codex":[0.9956909,0.0005235091,0.0003108929,0.001141913,0.001221585,0.001111208],"domain_scores_gemma":[0.9963918,0.0005430554,0.0001809685,0.001643282,0.0009046405,0.0003361992],"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":[0.0004903011,0.002058195,0.006420203,0.0003860295,0.0002531703,0.004822082,0.001507292,0.00161235,0.04382955,0.8265626,0.07830565,0.03375262],"study_design_scores_gemma":[0.003246598,0.0008456063,0.006835271,0.0006310427,0.00004032346,0.0001791128,0.0033521,0.0439379,0.02768331,0.01417577,0.8976936,0.001379406],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1324088,0.002412685,0.7308151,0.003475295,0.003595393,0.002831756,0.000076489,0.001475883,0.1229086],"genre_scores_gemma":[0.9016179,0.0003866776,0.03144005,0.00009524691,0.001602464,0.00003304801,0.00008697344,0.00007806958,0.06465961],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8193879,"threshold_uncertainty_score":0.9999468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03750431777709179,"score_gpt":0.272280257716205,"score_spread":0.2347759399391132,"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."}}