{"id":"W3045311751","doi":"10.1038/s41587-020-0640-y","title":"Author Correction: A robust benchmark for detection of germline large deletions and insertions","year":2020,"lang":"en","type":"erratum","venue":"Nature Biotechnology","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Genome British Columbia; Centre Hospitalier Universitaire Sainte-Justine; Ontario Institute for Cancer Research","funders":"","keywords":"Germline; Benchmark (surveying); Computational biology; Genetics; Computer science; Biology; Gene; Geography; Cartography","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","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0001034475,0.0002731042,0.0003798239,0.0001622233,0.0001890135,0.00000938864,0.0001998206,0.0039694,0.000004619604],"category_scores_gemma":[0.0004422036,0.0002768178,0.0001617721,0.0002096861,0.0001617076,6.061273e-7,0.0002362559,0.001016498,8.439285e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001949862,"about_ca_system_score_gemma":0.000112439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001054272,"about_ca_topic_score_gemma":0.000362995,"domain_scores_codex":[0.9987483,0.00002620986,0.0002978252,0.0005916961,0.00007847742,0.0002575124],"domain_scores_gemma":[0.9991222,0.00002176934,0.0002327548,0.000364616,0.0002062291,0.00005241845],"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":[0.0001027601,0.00006742663,0.00003241129,0.0001357861,0.0002922331,0.000001066765,0.00003003704,0.000008248276,0.4131837,0.0003397526,0.5821461,0.0036605],"study_design_scores_gemma":[0.0003578895,0.0006874701,0.0005096479,0.00002552269,0.000128096,0.00002898736,0.00006626165,0.0002941778,0.05974088,0.0001691152,0.9377447,0.0002472718],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.1600687,0.4161719,0.1334571,0.05740282,0.2120968,0.007779903,0.008485993,0.0003676881,0.004169145],"genre_scores_gemma":[0.9506047,0.01306369,0.005375486,0.0008575858,0.003838157,0.0003544957,0.002561418,0.0001374453,0.02320703],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.790536,"threshold_uncertainty_score":0.9999684,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008414670882417824,"score_gpt":0.2431757460537416,"score_spread":0.2347610751713238,"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."}}