{"id":"W1515132277","doi":"10.1007/978-3-540-27868-9_26","title":"Dictionary-Based Syntactic Pattern Recognition Using Tries","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Trie; Levenshtein distance; Computer science; String (physics); Prefix; Edit distance; Set (abstract data type); Computation; Representation (politics); Element (criminal law); Algorithm; Substitution (logic); Theoretical computer science; Data structure; Mathematics; 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":[0.0004741811,0.0005020805,0.0004292317,0.0008181632,0.000444767,0.0006390407,0.002003131,0.0003081947,0.00006565265],"category_scores_gemma":[0.00004260402,0.0004590816,0.00013262,0.0005118505,0.0004289477,0.001199866,0.0008813843,0.0007152634,0.0000571133],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005785117,"about_ca_system_score_gemma":0.001038097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001137538,"about_ca_topic_score_gemma":0.00002377763,"domain_scores_codex":[0.9964433,0.00003716901,0.0004903753,0.001449125,0.001034673,0.0005453864],"domain_scores_gemma":[0.9977345,0.000326965,0.0003412647,0.001188575,0.0002411506,0.0001675423],"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":[0.000005651841,0.00003858929,0.00003134094,0.00004043097,0.000006678331,0.0001160202,0.0001286141,0.04280249,0.00006511318,0.0004876139,0.000006149509,0.9562713],"study_design_scores_gemma":[0.0003985247,0.0001500599,0.00009128223,0.001110644,0.0000132032,0.00009259894,1.00318e-7,0.8866921,0.00109648,0.1093354,0.0003701282,0.000649429],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001424965,0.0002514024,0.9961538,0.0003990304,0.002147915,0.0003059938,0.00002940211,0.0002039443,0.0003659923],"genre_scores_gemma":[0.1415745,0.00003767419,0.8556629,0.001829651,0.0007437986,0.00001034328,0.00006017518,0.00005176042,0.00002913461],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9556219,"threshold_uncertainty_score":0.9997861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03047022484350267,"score_gpt":0.2534543415989348,"score_spread":0.2229841167554321,"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."}}