{"id":"W1990193053","doi":"10.1186/1471-2105-11-s1-s11","title":"Fast motif recognition via application of statistical thresholds","year":2010,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; University of Waterloo","funders":"","keywords":"Pairwise comparison; Hamming distance; Computer science; String (physics); String metric; Bounded function; Motif (music); Bottleneck; Theoretical computer science; String searching algorithm; Algorithm; Mathematics; Artificial intelligence; Pattern matching; Physics","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":[],"consensus_categories":[],"category_scores_codex":[0.000135447,0.0001012414,0.0001035416,0.00003113798,0.0000393123,0.00001526135,0.0001421006,0.0001593458,0.00002122184],"category_scores_gemma":[0.00004441194,0.00009772986,0.00004549554,0.00005092346,0.0000818737,0.000005134433,0.00006481876,0.00009154152,0.00004172048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004836732,"about_ca_system_score_gemma":0.00004084141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008322966,"about_ca_topic_score_gemma":0.00009823092,"domain_scores_codex":[0.9993245,0.000007712573,0.0003293729,0.0001011764,0.0001017482,0.0001354739],"domain_scores_gemma":[0.9993571,0.0000123671,0.0001618633,0.0003145871,0.0000956446,0.0000584235],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009788574,0.0003030867,0.01792128,0.0006163032,0.00007308678,4.154633e-7,0.0003035741,0.001002976,0.7322437,0.004182467,0.001076931,0.2421783],"study_design_scores_gemma":[0.0007117605,0.0002556023,0.009620008,0.000008720986,0.00004052488,0.00004737184,0.0001966669,0.9395318,0.04392756,0.002694493,0.002605005,0.0003604933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3869792,0.000005975973,0.61119,0.000006660014,0.00008729819,0.0001308275,0.000118069,0.000007576488,0.001474488],"genre_scores_gemma":[0.6662581,0.00001001135,0.332555,0.00004419416,0.00007206654,0.00001281446,0.001009547,0.00001217653,0.00002606975],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9385288,"threshold_uncertainty_score":0.3985308,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006674848646587189,"score_gpt":0.2252408335003245,"score_spread":0.2185659848537373,"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."}}