{"id":"W4366818125","doi":"10.1093/nargab/lqad038","title":"SUP: a probabilistic framework to propagate genome sequence uncertainty, with applications","year":2023,"lang":"en","type":"article","venue":"NAR Genomics and Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Agency of Canada; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Western University","keywords":"Resampling; Computer science; Propagation of uncertainty; Probabilistic logic; Sequence (biology); Representation (politics); Variance (accounting); Algorithm; Data mining; Artificial intelligence; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001845222,0.0001970669,0.0001729726,0.0000736495,0.0002105394,0.00006874185,0.0002076536,0.0001062623,0.000002311855],"category_scores_gemma":[0.0000277732,0.0001589012,0.00003751061,0.0002839564,0.0001158164,0.000001707993,0.0002002329,0.00008400682,0.00005468295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002377794,"about_ca_system_score_gemma":0.00009932439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007332177,"about_ca_topic_score_gemma":0.0000185949,"domain_scores_codex":[0.9990121,0.00001075714,0.000267089,0.0002612501,0.0001112671,0.0003375415],"domain_scores_gemma":[0.9992644,0.00002058086,0.00008122452,0.0003704992,0.0001110745,0.000152278],"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":[0.0007446606,0.0002882732,0.006850411,0.001730748,0.001113858,0.00002403007,0.01435043,0.1448759,0.7101156,0.01745198,0.003474927,0.0989793],"study_design_scores_gemma":[0.001451663,0.002740213,0.006467366,0.0001062783,0.0001769657,0.0001658731,0.003451917,0.03006088,0.004274028,0.009587438,0.9393823,0.002135035],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9877435,0.0003003085,0.009359669,0.0004608966,0.00005232721,0.001209049,0.0002960319,0.00002560375,0.0005525929],"genre_scores_gemma":[0.9449559,0.002094392,0.05055433,0.000907774,0.0002499554,0.0003935381,0.0003085048,0.00004902147,0.0004865296],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9359074,"threshold_uncertainty_score":0.6479805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01576609938421404,"score_gpt":0.2448559491589844,"score_spread":0.2290898497747704,"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."}}