{"id":"W2918262247","doi":"10.1101/534552","title":"SimRVSequences: an R package to simulate genetic sequence data for pedigrees","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; Simon Fraser University","funders":"","keywords":"Pedigree chart; Sample (material); R package; Computer science; Software package; Sequence (biology); Sample size determination; Software; Data mining; Computational biology; Genetics; Biology; Statistics; Mathematics; Gene; Programming language","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":[0.008101823,0.003361029,0.002993114,0.002613091,0.0008019969,0.002807299,0.005649321,0.001361694,0.07501561],"category_scores_gemma":[0.03426799,0.003004645,0.002943649,0.002521094,0.001107903,0.001920805,0.003420847,0.003741588,0.05356308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008540294,"about_ca_system_score_gemma":0.003175931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005134211,"about_ca_topic_score_gemma":0.004857777,"domain_scores_codex":[0.9963443,0.001967849,0.0003271064,0.0005666587,0.000580997,0.0002131016],"domain_scores_gemma":[0.9866589,0.009876153,0.00075181,0.001434375,0.0008969458,0.0003817677],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001176673,0.0001586351,0.01227537,0.003460763,0.002097238,0.001020142,0.0009310205,0.1063361,0.004052147,0.03418761,0.7465261,0.08777817],"study_design_scores_gemma":[0.00173839,0.0002557689,0.00537355,0.000804569,0.0006795417,0.0009827275,0.0001818471,0.4125806,0.008562109,0.1039917,0.4644645,0.0003847249],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.006090112,0.0006059404,0.5386659,0.0007623464,0.0004223194,0.0003622544,0.09236651,0.3554143,0.005310398],"genre_scores_gemma":[0.06398412,0.0009894882,0.5742494,0.001034682,0.0002566482,0.003224302,0.1212993,0.2284143,0.006547732],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.07501561,"threshold_uncertainty_score":0.2509522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04896912718368149,"score_gpt":0.2999840916832112,"score_spread":0.2510149644995296,"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."}}