{"id":"W4409171383","doi":"10.1186/s13100-025-00353-0","title":"REPrise: de novo interspersed repeat detection using inexact seeding","year":2025,"lang":"en","type":"article","venue":"Mobile DNA","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Japan Society for the Promotion of Science; Institute of Genetics; Japan Agency for Medical Research and Development","keywords":"Reprise; Biology; Genetics; Human genetics; Seeding; Computational biology; Evolutionary biology; Humanities; Gene; Philosophy","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001258651,0.00009837308,0.00009577427,0.00004360754,0.00009801019,0.00001928331,0.00008933665,0.00008411948,0.000006181574],"category_scores_gemma":[0.00005463941,0.0001011917,0.00006874918,0.00008197284,0.00003404407,5.554795e-7,0.0001247553,0.00005274612,0.000001450719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004583902,"about_ca_system_score_gemma":0.00003903563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000391616,"about_ca_topic_score_gemma":0.0000222947,"domain_scores_codex":[0.9993842,0.00002281336,0.0001286629,0.0002605173,0.00003623682,0.0001675211],"domain_scores_gemma":[0.9996459,0.000007747502,0.00003769492,0.0002334631,0.00004709976,0.00002802466],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003195523,0.00001691948,0.004234762,0.0000105305,0.00005543735,0.000001755598,0.00009513973,0.0004967015,0.9916123,0.00000588177,0.00007467008,0.003363912],"study_design_scores_gemma":[0.000380227,0.0001217836,0.00351901,0.00002172501,0.00003585118,0.00001823808,0.0005671445,0.0008664418,0.9688052,0.00007790625,0.0254314,0.0001550704],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99336,0.0007639696,0.003796195,0.00002605229,0.0002731617,0.0001384927,0.00000389685,0.000006541753,0.001631655],"genre_scores_gemma":[0.9986992,0.0001203972,0.0004165459,0.0001506074,0.0001338776,0.00002730931,0.000004373768,0.00001082198,0.0004368778],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02535673,"threshold_uncertainty_score":0.4126479,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00864695074304063,"score_gpt":0.2639406472106702,"score_spread":0.2552936964676296,"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."}}