{"id":"W4319596421","doi":"10.1038/s41598-023-29354-w","title":"Author Correction: A high-throughput skim-sequencing approach for genotyping, dosage estimation and identifying translocations","year":2023,"lang":"en","type":"erratum","venue":"Scientific Reports","topic":"Biological Research and Disease Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Genotyping; Throughput; Chromosomal translocation; Computer science; Estimation; Computational biology; Genetics; Biology; Genotype; Gene; Telecommunications","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":[],"consensus_categories":[],"category_scores_codex":[0.001256581,0.0002741776,0.0003012082,0.0001531252,0.001015038,0.0003873877,0.0001551985,0.0004367311,0.00001118807],"category_scores_gemma":[0.001658709,0.0002403024,0.0001866529,0.0003451761,0.0004538745,0.00001593819,0.0001810218,0.0002627517,0.000005368711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006366548,"about_ca_system_score_gemma":0.0006116225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006249166,"about_ca_topic_score_gemma":0.00009317495,"domain_scores_codex":[0.9971682,0.00006845161,0.000476976,0.0014166,0.0003879759,0.0004817579],"domain_scores_gemma":[0.9985111,0.00003378921,0.0002742037,0.0006436312,0.0003445297,0.0001927141],"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":[0.00002775329,0.00003858585,0.00002844272,0.0004136797,0.0001172515,0.00002363835,0.0001005642,0.0001661576,0.01288286,0.0000377483,0.9798544,0.006308938],"study_design_scores_gemma":[0.0007221913,0.0005453924,0.001808037,0.0005567957,0.0005117758,0.0002999491,0.001105078,0.02271718,0.01484305,0.02143607,0.9336106,0.001843881],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.01182094,0.03076003,0.7111977,0.0009733256,0.2265371,0.006000892,0.0004949401,0.0005133218,0.01170173],"genre_scores_gemma":[0.1671008,0.000852479,0.01535862,0.00008303966,0.002746427,0.001619308,0.0294378,0.0001320413,0.7826695],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.7709678,"threshold_uncertainty_score":0.9799247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05307283238839311,"score_gpt":0.3340360111257432,"score_spread":0.28096317873735,"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."}}