{"id":"W3121331813","doi":"10.1101/2021.01.15.426843","title":"The GA4GH Variation Representation Specification (VRS): a Computational Framework for the Precise Representation and Federated Identification of Molecular Variation","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Genomics","funders":"U.S. National Library of Medicine; National Cancer Institute; National Institutes of Health; European Molecular Biology Laboratory; Wellcome Trust; Invitae; Center for Individualized Medicine, Mayo Clinic; Mayo Clinic","keywords":"Variation (astronomy); Documentation; Computer science; Terminology; Representation (politics); Leverage (statistics); Identification (biology); Information retrieval; Data science; Artificial intelligence","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":[0.01252555,0.001366985,0.001250943,0.004201971,0.001387637,0.0088189,0.005000472,0.002192656,0.00572528],"category_scores_gemma":[0.02342418,0.00119786,0.004379502,0.004337443,0.003408307,0.006980683,0.007887659,0.003338106,0.002039456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002410588,"about_ca_system_score_gemma":0.004441182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01346589,"about_ca_topic_score_gemma":0.01259908,"domain_scores_codex":[0.9919442,0.003361374,0.001082602,0.001163291,0.002081742,0.0003668641],"domain_scores_gemma":[0.9903019,0.003806951,0.0005055225,0.003796618,0.00131818,0.0002707728],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001541973,0.00004538197,0.001980146,0.0003454078,0.0001076321,0.0003391747,0.0007818997,0.07312989,0.001821704,0.8368226,0.02577225,0.05869981],"study_design_scores_gemma":[0.00007025034,0.00004741849,0.0002768753,0.0003140406,0.00007276027,0.0003201413,0.0003111456,0.3095036,0.005415806,0.5393077,0.1442563,0.0001039394],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.001339694,0.0001014351,0.9886392,0.0004480772,0.00005782376,0.00008861241,0.001961517,0.005676507,0.001687149],"genre_scores_gemma":[0.04251755,0.0003452236,0.9410037,0.00039678,0.00006697163,0.0004081392,0.01144884,0.00189713,0.001915594],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.01346589,"threshold_uncertainty_score":0.06624222,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01385694548770191,"score_gpt":0.2557270555399465,"score_spread":0.2418701100522446,"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."}}