{"id":"W2991026871","doi":"10.1093/bioinformatics/btz881","title":"SimRVSequences: an R package to simulate genetic sequence data for pedigrees","year":2019,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Statistical Sciences Institute; Canadian Institutes of Health Research","keywords":"Pedigree chart; R package; Sequence (biology); Computer science; Sample (material); Data mining; Computational biology; Genetics; Biology; Programming language; Gene","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":[0.0004048906,0.0001612015,0.0001973324,0.000042693,0.00006996531,0.00003923261,0.0007215249,0.0001642805,0.00002188627],"category_scores_gemma":[0.0003681246,0.0001455053,0.00004980415,0.00008415786,0.00003734251,0.00001779655,0.0002795968,0.00004754034,0.0001167896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001629992,"about_ca_system_score_gemma":0.0001175002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001922359,"about_ca_topic_score_gemma":0.00003575247,"domain_scores_codex":[0.9987813,0.00003522562,0.0003996288,0.0003084424,0.0001123426,0.0003631105],"domain_scores_gemma":[0.9982842,0.00004763746,0.0001455611,0.001259357,0.0001163492,0.0001468836],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004618587,0.0004833645,0.2578002,0.001087011,0.0007518486,0.000006685833,0.003386126,0.0881722,0.400963,0.001304991,0.0816677,0.163915],"study_design_scores_gemma":[0.002216225,0.003506847,0.03006967,0.00006201685,0.0001369416,0.00004276471,0.001748293,0.6250986,0.006039649,0.0009772762,0.3283607,0.001740974],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9233523,0.0000835289,0.07421529,0.0002041854,0.0002697881,0.0007148106,0.0006443507,0.0000235006,0.0004922405],"genre_scores_gemma":[0.77042,0.00006833224,0.2253904,0.001515264,0.0002178525,0.00002685076,0.001873175,0.0000225141,0.0004656714],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5369264,"threshold_uncertainty_score":0.5933535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0579715405356776,"score_gpt":0.3316623543441026,"score_spread":0.273690813808425,"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."}}