{"id":"W2997026609","doi":"10.1038/s41587-019-0395-5","title":"Author Correction: Visualizing structure and transitions in high-dimensional biological data","year":2020,"lang":"en","type":"article","venue":"Nature Biotechnology","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; Université du Québec; Mila - Quebec Artificial Intelligence Institute","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; Defense Advanced Research Projects Agency; U.S. Department of Health and Human Services; National Institutes of Health; Alfred P. Sloan Foundation; Gruber Foundation; U.S. Department of Defense","keywords":"Computer science; Computational biology; Data science; Information retrieval; Biology","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":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00009255172,0.0001335121,0.0001609778,0.00004988939,0.00006363779,0.00001261033,0.000264655,0.001536209,0.00001993301],"category_scores_gemma":[0.0001057816,0.0001145097,0.00002056297,0.0001738771,0.0001375458,0.000004924358,0.0003133511,0.0007034869,0.000002265242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006877374,"about_ca_system_score_gemma":0.000031546,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004144569,"about_ca_topic_score_gemma":0.00004661323,"domain_scores_codex":[0.9991207,0.00002912613,0.0001938279,0.0004111276,0.00005690036,0.0001883157],"domain_scores_gemma":[0.9995486,0.00000937185,0.00005228103,0.0003091626,0.00001938417,0.00006122558],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003590812,0.0000806678,0.001454482,0.00004225047,0.0001254723,0.00003387696,0.0001766883,0.0002694765,0.8719884,0.01048223,0.06253257,0.05245477],"study_design_scores_gemma":[0.005450567,0.00289235,0.0142958,0.0001078461,0.0001103644,0.0009600355,0.0009159644,0.09787506,0.2170578,0.005368924,0.6527701,0.002195238],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.95367,0.003232002,0.00463521,0.03713667,0.0006993786,0.0002314483,0.0002828066,0.00006936315,0.00004307217],"genre_scores_gemma":[0.9924732,0.00007622255,0.003666246,0.002728154,0.0002228885,0.000002277215,0.0008059401,0.000009268426,0.00001584669],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6549307,"threshold_uncertainty_score":0.99976,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01622277694052169,"score_gpt":0.2673593857525572,"score_spread":0.2511366088120355,"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."}}