{"id":"W2000098237","doi":"10.1007/s002360050001","title":"Querying sequence databases with transducers","year":2000,"lang":"en","type":"article","venue":"Acta Informatica","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Datalog; Computer science; Ackermann function; Subroutine; Theory of computation; Theoretical computer science; Hierarchy; Programming language; Sequence (biology); Database; P; Query language; Workstation; Transducer; Time complexity; Algorithm; Mathematics","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":[0.0001056636,0.0001123881,0.000107469,0.0000440857,0.0001488211,0.0001595698,0.0007058589,0.00001957327,0.0003067901],"category_scores_gemma":[0.000007832563,0.00007755527,0.00002078219,0.0002182821,0.00004498599,0.003991719,0.00007485667,0.0001055707,0.0001892796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001667901,"about_ca_system_score_gemma":0.00006792666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006135127,"about_ca_topic_score_gemma":0.000003621097,"domain_scores_codex":[0.9991435,0.00001327131,0.0001969477,0.0001417855,0.0002643028,0.0002401258],"domain_scores_gemma":[0.9991333,0.00005212207,0.00004440681,0.0006498146,0.00002370922,0.00009659927],"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":[0.00003368661,0.00006110583,0.0001059498,0.00007942283,0.00002972965,0.00003740741,0.004251197,0.0003689054,0.0004002207,0.01234669,0.006820628,0.9754651],"study_design_scores_gemma":[0.0009137453,0.0002268535,0.000976856,0.0003241512,0.00001648202,0.0002956993,0.0002166365,0.4387614,0.002708432,0.0003492337,0.5544991,0.0007114382],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1063875,0.00005094942,0.8179498,0.001594532,0.0001496897,0.0003757466,0.00006561795,0.0006207116,0.07280545],"genre_scores_gemma":[0.7161655,0.00008098142,0.2820602,0.00134436,0.00003487543,0.00001707959,0.00005576751,0.000008225936,0.000232999],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9747536,"threshold_uncertainty_score":0.3359136,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02632146965480677,"score_gpt":0.2518736810062855,"score_spread":0.2255522113514788,"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."}}