{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001055332,0.0008847383,0.001684254,0.00197903,0.001226519,0.002893417,0.001274138,0.001318698,0.009185466],"category_scores_gemma":[0.009189859,0.0005524816,0.001623201,0.001893724,0.001519574,0.005722618,0.002336319,0.001310566,0.004230509],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001063684,"about_ca_system_score_gemma":0.001679086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00181665,"about_ca_topic_score_gemma":0.003234365,"domain_scores_codex":[0.9982296,0.0001914418,0.0002605133,0.0005956262,0.0004372028,0.0002856776],"domain_scores_gemma":[0.9968226,0.001453266,0.0002625326,0.0007176321,0.0005925964,0.000151418],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.002675778,0.0002682674,0.009067236,0.001364529,0.0001942323,0.00159836,0.002289623,0.0551142,0.1098499,0.2462787,0.009719708,0.5615795],"study_design_scores_gemma":[0.0001377365,0.0005842911,0.002289074,0.0003396268,0.0001135345,0.0009383694,0.0009794848,0.2964989,0.100758,0.5637251,0.03341923,0.0002166976],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09644933,0.0005593869,0.8887429,0.0002508037,0.0001649159,0.0001237382,0.0008690426,0.005312709,0.00752713],"genre_scores_gemma":[0.3333906,0.0002762981,0.6539387,0.0002660975,0.0001076667,0.0001691716,0.002948689,0.001021518,0.007881163],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009185466,"threshold_uncertainty_score":0.03072846,"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."}}