{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002875903,0.001591305,0.00114427,0.001662999,0.0006423527,0.001391463,0.00241955,0.001598918,0.00440382],"category_scores_gemma":[0.008740181,0.0009655466,0.001595467,0.001014707,0.0008663677,0.001861547,0.002136605,0.001873558,0.002375583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000523768,"about_ca_system_score_gemma":0.0008958696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001151937,"about_ca_topic_score_gemma":0.001801969,"domain_scores_codex":[0.9983284,0.0002747465,0.0001258892,0.0006135585,0.0005738384,0.00008348284],"domain_scores_gemma":[0.9960231,0.002299569,0.000499234,0.0005856564,0.0004614663,0.0001309267],"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":[0.002726389,0.0005221073,0.01752792,0.001936726,0.001067733,0.001596149,0.001069732,0.1070982,0.3260271,0.01212046,0.03698759,0.49132],"study_design_scores_gemma":[0.0001953911,0.0003948116,0.004614948,0.00007281428,0.0001033154,0.0009856749,0.0001076246,0.7712993,0.1921029,0.007769046,0.0221658,0.0001882956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04959121,0.0004037878,0.8676873,0.00009592383,0.0001003034,0.0001737003,0.001505522,0.07906345,0.001378876],"genre_scores_gemma":[0.127304,0.0001629261,0.8600557,0.0001771893,0.00003699836,0.0003115448,0.004432324,0.005763998,0.001755237],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00440382,"threshold_uncertainty_score":0.01520944,"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."}}