{"id":"W2914488418","doi":"10.1186/s12859-019-2611-1","title":"sim1000G: a user-friendly genetic variant simulator in R for unrelated individuals and family-based designs","year":2019,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute; Public Health Ontario; University of Toronto; Mount Sinai Hospital","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Linkage disequilibrium; Population; Context (archaeology); Pedigree chart; Computer science; Genetics; Biology; Haplotype; Allele","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":[0.0005076398,0.0001892939,0.0002758353,0.0001012126,0.00005740906,0.00002817835,0.0001581528,0.0003413209,0.00001375435],"category_scores_gemma":[0.0003056579,0.000176335,0.00007946468,0.0001263023,0.0000464776,0.000006710309,0.0000745092,0.00008375905,0.00002395733],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002319479,"about_ca_system_score_gemma":0.0001993742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001332687,"about_ca_topic_score_gemma":0.00002397474,"domain_scores_codex":[0.9986807,0.00007459887,0.0005512406,0.0002248267,0.00009740151,0.0003712596],"domain_scores_gemma":[0.9991241,0.0001800975,0.0002090834,0.0003231648,0.00007545305,0.00008807799],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001865105,0.0002021498,0.7930681,0.0004667369,0.0001709892,0.000001393781,0.0003819446,0.1864044,0.01214445,0.0005922252,0.004074026,0.002307152],"study_design_scores_gemma":[0.004209607,0.0009448563,0.2416711,0.00004341017,0.00006028402,0.000008338874,0.0004351139,0.7347952,0.001061065,0.0002058257,0.01605253,0.0005126327],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7560434,0.0001988598,0.2426372,0.00003775118,0.00009780715,0.000729139,0.00007305884,0.00001227806,0.0001705118],"genre_scores_gemma":[0.7146267,0.00003944428,0.2843123,0.0005691352,0.00003270656,0.00005147769,0.0001945397,0.00002118737,0.0001524743],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.551397,"threshold_uncertainty_score":0.7190731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01897153664653678,"score_gpt":0.2630115897239511,"score_spread":0.2440400530774143,"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."}}