{"id":"W2067201268","doi":"10.1142/s0219720004000788","title":"IDENTIFYING UNIFORMLY MUTATED SEGMENTS WITHIN REPEATS","year":2004,"lang":"en","type":"article","venue":"Journal of Bioinformatics and Computational Biology","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Pacific Institute for the Mathematical Sciences; Simon Fraser University","funders":"","keywords":"Mathematics; String (physics); Coin flipping; Combinatorics; Set (abstract data type); Mutation; Prior probability; Algorithm; Mutation rate; Shuffling; Discrete mathematics; Statistics; Computer science; Genetics; Biology; Bayesian probability","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001315952,0.0005599692,0.001105489,0.001787761,0.000554561,0.001421001,0.00174667,0.00186379,0.002251699],"category_scores_gemma":[0.01087062,0.0005894596,0.0007587383,0.001027953,0.0007311748,0.001050948,0.00123642,0.0008353481,0.001172262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009995907,"about_ca_system_score_gemma":0.001123643,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002078149,"about_ca_topic_score_gemma":0.002236452,"domain_scores_codex":[0.9984819,0.0003028771,0.00009269864,0.0006037238,0.0003484176,0.0001703208],"domain_scores_gemma":[0.9955094,0.002475505,0.0008487059,0.000497071,0.0004875172,0.0001817963],"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":[0.002135699,0.0002833189,0.05975987,0.0003805193,0.0002743416,0.001882478,0.0009331544,0.2561268,0.1775128,0.03187162,0.003079851,0.4657597],"study_design_scores_gemma":[0.00004120711,0.0001017135,0.004183027,0.00002655618,0.00003318548,0.0004527557,0.0001177669,0.9344926,0.03791112,0.02104676,0.001552132,0.00004106188],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2448474,0.0001564058,0.7491282,0.0001536642,0.0000257431,0.0001730942,0.0004439963,0.003209625,0.001861879],"genre_scores_gemma":[0.4742921,0.00008319754,0.5215225,0.00009525644,0.00001807814,0.0001652228,0.001157131,0.0003338221,0.002332738],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002251699,"threshold_uncertainty_score":0.007532656,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01715622109661268,"score_gpt":0.2722669738252626,"score_spread":0.2551107527286499,"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."}}