{"id":"W3212791962","doi":"10.1016/j.xgen.2021.100027","title":"The GA4GH Variation Representation Specification: A computational framework for variation representation and federated identification","year":2021,"lang":"en","type":"article","venue":"Cell Genomics","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Genomics","funders":"U.S. National Library of Medicine; National Cancer Institute; National Institutes of Health; Invitae; European Molecular Biology Laboratory; European Bioinformatics Institute; Wellcome Trust; National Human Genome Research Institute; University of Utah; Center for Individualized Medicine, Mayo Clinic; Mayo Clinic","keywords":"Variation (astronomy); Computer science; Identifier; Representation (politics); Identification (biology); Terminology; External Data Representation; Schema (genetic algorithms); Data science; Information retrieval; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021739,0.0001113573,0.00008718683,0.00002620525,0.0005147384,0.0003036806,0.00008988491,0.0001210874,0.000007544441],"category_scores_gemma":[0.0002796274,0.0001120073,0.0000676963,0.0001270462,0.00003960814,0.00001259511,0.00004788915,0.0000524653,0.000007162311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000401481,"about_ca_system_score_gemma":0.0001598911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000754275,"about_ca_topic_score_gemma":0.000009055687,"domain_scores_codex":[0.9988859,0.00008995519,0.0003323987,0.0004423461,0.0001111598,0.0001382997],"domain_scores_gemma":[0.9988053,0.0001414492,0.0002489623,0.0003006756,0.0004495869,0.0000539606],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0002758335,0.000154578,0.0007513231,0.00004249766,0.0001195006,0.000002441793,0.0008286142,0.01349405,0.9596868,0.01641009,0.00179323,0.006441022],"study_design_scores_gemma":[0.003156204,0.0002215924,0.378644,0.00002608514,0.0003260321,0.0001139474,0.003126424,0.08515063,0.325223,0.1664951,0.03648842,0.001028591],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4177686,0.000959167,0.5792731,0.0008138677,0.0004631499,0.0004719847,0.00005931423,0.00001396593,0.0001768381],"genre_scores_gemma":[0.9757478,0.0008446382,0.01907204,0.000164305,0.0004466068,0.00009895807,0.002944962,0.00002636738,0.0006542907],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6344638,"threshold_uncertainty_score":0.4567526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01717231562835898,"score_gpt":0.2732130026309989,"score_spread":0.2560406870026399,"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."}}