{"id":"W2897346898","doi":"10.1002/humu.23625","title":"ClinGen advancing genomic data‐sharing standards as a GA4GH driver project","year":2018,"lang":"en","type":"article","venue":"Human Mutation","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Genomics; Ontario Institute for Cancer Research","funders":"National Cancer Institute; NIH Office of the Director; Wellcome Trust; Ontario Institute for Cancer Research; National Human Genome Research Institute; National Heart, Lung, and Blood Institute; Broad Institute; National Institutes of Health; Government of Canada; Canadian Institutes of Health Research; Wellcome; European Molecular Biology Laboratory; Genome Canada","keywords":"Data sharing; Data science; Genomics; Set (abstract data type); Computer science; Resource (disambiguation); Biology; Knowledge management; Computational biology; Genome; Genetics; Medicine; Gene","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":[0.0002497067,0.0001015513,0.00007746055,0.00004438158,0.0002036544,0.00005613058,0.0002736571,0.00005735112,0.00005861541],"category_scores_gemma":[0.00007498168,0.00010371,0.00003550087,0.00004002834,0.00005597432,0.000008971456,0.0002525113,0.00003741338,0.00002859872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000363801,"about_ca_system_score_gemma":0.0001789336,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006397534,"about_ca_topic_score_gemma":0.0001585715,"domain_scores_codex":[0.9990932,0.00002115908,0.0001580291,0.0004304768,0.0001161065,0.0001810177],"domain_scores_gemma":[0.9992441,0.000003028143,0.00007996327,0.0004775519,0.000148418,0.00004693148],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001384263,0.00006560688,0.003654072,0.00004527906,0.0001069824,0.00002850853,0.000714716,0.00007675632,0.9835658,0.0002774901,0.006093722,0.005232622],"study_design_scores_gemma":[0.006149986,0.003812625,0.08486384,0.0002133287,0.0005167363,0.0002651661,0.002948382,0.005212063,0.3968369,0.00631647,0.4903286,0.002535947],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962692,0.0002841965,0.001136926,0.00001112357,0.000117557,0.0001873211,0.0001192238,0.00001740831,0.001856992],"genre_scores_gemma":[0.9967169,0.00003893522,0.0007966143,0.0001559331,0.0006282474,0.000008878331,0.001349773,0.00002487276,0.0002798216],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5867289,"threshold_uncertainty_score":0.4229169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03079718774591123,"score_gpt":0.3588544409860813,"score_spread":0.3280572532401701,"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."}}