{"id":"W4366992370","doi":"10.1093/bioinformatics/btad163","title":"Correction to: Continuous chromatin state feature annotation of the human epigenome","year":2023,"lang":"en","type":"erratum","venue":"Bioinformatics","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Michael Smith Health Research BC; Compute Canada; Genome Canada","keywords":"Epigenome; Chromatin; Feature (linguistics); Annotation; Computer science; State (computer science); Computational biology; Artificial intelligence; Biology; Genetics; DNA methylation; Programming language; DNA; Gene","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004045603,0.0004013461,0.0004297265,0.0001749251,0.0002272886,0.00008009225,0.0007004401,0.0005196983,0.00001391234],"category_scores_gemma":[0.0007402665,0.0003142615,0.0002446856,0.000425212,0.0001065111,0.00001107915,0.000443973,0.0007469481,0.0001185974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006513208,"about_ca_system_score_gemma":0.0002809875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005259089,"about_ca_topic_score_gemma":0.0001814418,"domain_scores_codex":[0.9979796,0.00007339393,0.0008645983,0.00020274,0.0005009128,0.0003787788],"domain_scores_gemma":[0.9975178,0.00003131198,0.001145423,0.0009164793,0.000292642,0.00009629273],"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":[0.00001048668,0.00001734488,0.0002250454,0.0005316006,0.0000841945,4.111308e-7,0.001238062,0.0005173144,0.001814526,0.00001152172,0.9930583,0.002491154],"study_design_scores_gemma":[0.0004635704,0.0006164422,0.01180906,0.0007696472,0.00009934429,0.00002016629,0.0006249713,0.008210149,0.004872289,0.00004414931,0.971701,0.0007692227],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.1839245,0.001684672,0.02212065,0.002837797,0.3472988,0.01306891,0.005391295,0.001230136,0.4224432],"genre_scores_gemma":[0.0097892,0.0003110219,0.007031105,0.000985999,0.001807097,0.00009043031,0.008662861,0.000233125,0.9710892],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.5486459,"threshold_uncertainty_score":0.9999309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006974353029127545,"score_gpt":0.2547317770100916,"score_spread":0.247757423980964,"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."}}