{"id":"W4220782754","doi":"10.1186/s13059-022-02645-7","title":"Author Correction: Benchmarking transposable element annotation methods for creation of a streamlined, comprehensive pipeline","year":2022,"lang":"en","type":"erratum","venue":"Genome biology","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Transposable element; Benchmarking; Pipeline (software); Biology; Computational biology; Annotation; Human genetics; Genome; Genetics; Data science; Computer science; Gene; Programming language","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003766169,0.0002973739,0.0006085967,0.0003355137,0.00009847503,0.000009568373,0.0001895604,0.0004604931,0.0005086298],"category_scores_gemma":[0.00002835641,0.0003230111,0.0001798458,0.0002309345,0.00004268522,0.00003634637,0.00002697041,0.0003967978,0.000001278431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002089147,"about_ca_system_score_gemma":0.00007097619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003613071,"about_ca_topic_score_gemma":0.00001428311,"domain_scores_codex":[0.998503,0.0001772292,0.000611771,0.0003410398,0.00007630052,0.0002907165],"domain_scores_gemma":[0.9990842,0.0002124988,0.0002170188,0.0002527847,0.0001921053,0.00004138275],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001322437,0.00007809472,0.00002698187,0.00106492,0.0003999956,0.000001733609,0.0007496639,0.007637233,0.05986014,0.000327164,0.5541465,0.3755753],"study_design_scores_gemma":[0.0002408999,0.0005827963,0.00004884597,0.00003407993,0.0001272837,0.000005887972,0.00008720344,0.06220175,0.002414891,0.0005357585,0.933426,0.0002945802],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00004311871,0.005971511,0.9677306,0.00004366869,0.0207112,0.001066095,0.0007067379,0.0003342499,0.003392822],"genre_scores_gemma":[0.01009594,0.008570376,0.7559978,0.0002631044,0.009187111,0.004964952,0.115335,0.0005729329,0.09501273],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3792795,"threshold_uncertainty_score":0.9999222,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02890398508704715,"score_gpt":0.3353251674681216,"score_spread":0.3064211823810744,"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."}}