{"id":"W2902456400","doi":"10.1101/482059","title":"FDA-ARGOS: A Public Quality-Controlled Genome Database Resource for Infectious Disease Sequencing Diagnostics and Regulatory Science Research","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Viral gastroenteritis research and epidemiology","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. Army Medical Research Institute of Infectious Diseases; University of Colorado School of Medicine, Anschutz Medical Campus; U.S. National Library of Medicine; Defense Threat Reduction Agency; Hamilton Health Sciences Foundation; Public Health Agency; Children's National Hospital; British Columbia Centre for Disease Control; Public Health England; National Institutes of Health; U.S. Department of Health and Human Services; U.S. Department of Energy; U.S. Department of Defense","keywords":"In silico; Infectious disease (medical specialty); Database; Computer science; Biology; Medicine; Disease; Genetics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02084505,0.001206258,0.001350202,0.005781098,0.001155183,0.005424903,0.003411631,0.002207472,0.02566737],"category_scores_gemma":[0.04257219,0.0008843321,0.000944701,0.005497449,0.0008863643,0.003324939,0.003890076,0.002042359,0.02035931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001559939,"about_ca_system_score_gemma":0.007975766,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002184296,"about_ca_topic_score_gemma":0.001949345,"domain_scores_codex":[0.9890905,0.00283606,0.001368708,0.001548347,0.004600257,0.0005561869],"domain_scores_gemma":[0.9580821,0.01331759,0.006508995,0.01235378,0.007321321,0.002416156],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.004366735,0.0007885674,0.01744167,0.002651071,0.0003922115,0.0007669145,0.0007096122,0.009262006,0.05220362,0.03322073,0.6690236,0.2091731],"study_design_scores_gemma":[0.001234677,0.0004384409,0.01340231,0.0007171803,0.0001643675,0.0007486372,0.0003225172,0.02154527,0.04888639,0.02322504,0.8890895,0.0002255982],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.03214003,0.002359609,0.2152288,0.006050895,0.0009048011,0.001789998,0.5745168,0.1201937,0.04681539],"genre_scores_gemma":[0.0610322,0.0008663524,0.2168149,0.001170463,0.0002991271,0.001024431,0.7051889,0.008522765,0.005080934],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.02566737,"threshold_uncertainty_score":0.1102404,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08664050909197937,"score_gpt":0.3585622616822597,"score_spread":0.2719217525902803,"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."}}