{"id":"W4382517690","doi":"10.1101/2023.06.20.23291538","title":"Geographic variation of mutagenic exposures in kidney cancer genomes","year":2023,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Ontario Institute for Cancer Research","funders":"Ministerstvo Zdravotnictví Ceské Republiky; National Institute for Health and Care Research; National Cancer Institute; Cancer Research UK; Hospital de Câncer de Barretos; National Cancer Center Japan; Wellcome Trust; Japan Agency for Medical Research and Development; Hospital de Clínicas de Porto Alegre; Univerzita Karlova v Praze","keywords":"Incidence (geometry); Cancer; Kidney cancer; Biology; Genetics; Epidemiology; Medicine; Internal medicine","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":[0.0004743566,0.0001331338,0.0002793774,0.001381118,0.000319619,0.0005223695,0.0002028562,0.0002613057,0.001714219],"category_scores_gemma":[0.001349601,0.0001398622,0.0002685906,0.001831445,0.000258525,0.0001700675,0.0004839685,0.0002801776,0.0002354084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002792258,"about_ca_system_score_gemma":0.0001751084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002715145,"about_ca_topic_score_gemma":0.003315355,"domain_scores_codex":[0.9995182,0.0001058857,0.00003427648,0.0001936244,0.00008720495,0.00006082205],"domain_scores_gemma":[0.998995,0.0002127045,0.0004618043,0.0001107929,0.0001403991,0.00007932208],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002954773,0.00002715967,0.9253239,0.0001682107,0.0005367704,0.0003036967,0.0006447131,0.001275869,0.05415151,0.0006257248,0.0006228097,0.01602418],"study_design_scores_gemma":[0.000003346261,0.00003410921,0.9961004,0.000009248509,0.00006034714,0.0002853964,0.0002009087,0.000390332,0.001374839,0.0001371629,0.001397047,0.000006757247],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9951325,0.0007311385,0.001069786,0.00007272369,0.000007013964,0.000007955879,0.002126345,0.00002783043,0.0008247085],"genre_scores_gemma":[0.9975694,0.0002034267,0.0006238174,0.00003016113,0.000004451118,0.000004879421,0.001367485,0.000009661254,0.0001866672],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002715145,"threshold_uncertainty_score":0.005734682,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01737040479094906,"score_gpt":0.2679673811155563,"score_spread":0.2505969763246073,"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."}}