{"id":"W4311115345","doi":"10.1038/s41467-022-32329-6","title":"Author Correction: A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns","year":2022,"lang":"en","type":"erratum","venue":"Nature Communications","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"SickKids Foundation; BC Cancer Agency; University of British Columbia; Prostate Cancer Canada; Université de Montréal; Hospital for Sick Children; Lunenfeld-Tanenbaum Research Institute; Mount Sinai Hospital; University of Calgary; Princess Margaret Cancer Centre; Simon Fraser University; University of Ottawa; McGill University; McGill University and Génome Québec Innovation Centre; Toronto General Hospital; University of Toronto; University Health Network; Genome Canada; Canada's Michael Smith Genome Sciences Centre; Vector Institute; Institute of Cancer Research; Ontario Institute for Cancer Research","funders":"Medical Research Council","keywords":"Mutation; Computer science; Deep learning; Primary (astronomy); Artificial intelligence; Computational biology; Bioinformatics; Biology; Genetics; Gene","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002702144,0.001623248,0.001319227,0.002609805,0.002541861,0.002382364,0.002734704,0.005137337,0.05726243],"category_scores_gemma":[0.04598767,0.0007810058,0.001236127,0.001682004,0.00183133,0.001545154,0.001362859,0.007881373,0.03030881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002357741,"about_ca_system_score_gemma":0.003583507,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01568046,"about_ca_topic_score_gemma":0.01826035,"domain_scores_codex":[0.9972895,0.0003217742,0.0004363169,0.0004908984,0.001254115,0.0002074197],"domain_scores_gemma":[0.9761081,0.00668141,0.0006855126,0.001360033,0.01443525,0.0007295719],"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":[0.00004067315,0.000005779714,0.0001163051,0.0001004385,0.00001454853,0.0005669827,0.00003465177,0.00007728887,0.000107013,0.0006749107,0.9889804,0.009280895],"study_design_scores_gemma":[0.0000814138,0.00004008144,0.001142954,0.0004172312,0.00009976512,0.00351361,0.0001310643,0.0008248779,0.001878554,0.003267655,0.9885329,0.00006984829],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.0006384382,0.001260457,0.003059264,0.06121786,0.9251609,0.00003409135,0.002460046,0.0008484981,0.005320382],"genre_scores_gemma":[0.04854947,0.01004294,0.01693434,0.0972598,0.2507798,0.0002815929,0.007248476,0.003848719,0.5650549],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.05726243,"threshold_uncertainty_score":0.1915619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04290191066007118,"score_gpt":0.3581553867686085,"score_spread":0.3152534761085373,"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."}}