{"id":"W3087991744","doi":"10.3390/a13110294","title":"Computing Maximal Lyndon Substrings of a String","year":2020,"lang":"en","type":"article","venue":"Algorithms","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Substring; Suffix array; Suffix tree; Algorithm; String (physics); Generalized suffix tree; Time complexity; Mathematics; Suffix; Combinatorics; Compressed suffix array; String searching algorithm; Sorting; Computer science; Discrete mathematics; Data structure","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":[],"consensus_categories":[],"category_scores_codex":[0.0001587762,0.0001456872,0.000246873,0.00005513134,0.00009392713,0.00008138293,0.0009821309,0.00005292599,0.0000171628],"category_scores_gemma":[0.00003811215,0.0001353468,0.00007631976,0.000463483,0.00003567751,0.0004734987,0.0008062438,0.0001688672,0.0000307746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001514538,"about_ca_system_score_gemma":0.00004337924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006555879,"about_ca_topic_score_gemma":3.248884e-7,"domain_scores_codex":[0.9986068,0.00003276717,0.0003183275,0.0004138731,0.0003438151,0.0002843961],"domain_scores_gemma":[0.9991595,0.00007552447,0.0001554181,0.0003683575,0.00007505309,0.0001661823],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002075471,0.0001216266,0.001721222,0.00009689714,0.00005479352,0.0001679296,0.00267273,0.000644316,0.006680342,0.01501266,0.002049578,0.9707571],"study_design_scores_gemma":[0.0006221523,0.0001670089,0.002668374,0.0000533148,0.000006875734,0.00002771066,0.00006259247,0.9777567,0.0143795,0.0004200308,0.003593551,0.0002422155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03431583,0.000213058,0.9638263,0.0006034052,0.0002199847,0.0001071417,0.000009135811,0.0002007667,0.000504362],"genre_scores_gemma":[0.7408081,0.00001266424,0.2587143,0.0002519504,0.0001835844,0.000001402102,0.000006487997,0.00001040773,0.00001099373],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9771124,"threshold_uncertainty_score":0.5519283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02706747043696601,"score_gpt":0.2409447911328035,"score_spread":0.2138773206958375,"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."}}