{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","open_science"],"consensus_categories":[],"category_scores_codex":[0.19409,0.001365811,0.001212999,0.005045242,0.002424168,0.009781783,0.0107489,0.005653799,0.01106979],"category_scores_gemma":[0.1499862,0.001248788,0.001730889,0.005000191,0.003517111,0.01021604,0.01996794,0.006188006,0.007698076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007711606,"about_ca_system_score_gemma":0.0559012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01355552,"about_ca_topic_score_gemma":0.009017624,"domain_scores_codex":[0.9134063,0.04525926,0.00506477,0.005995027,0.02664083,0.003633845],"domain_scores_gemma":[0.7816617,0.04217268,0.00770126,0.06533148,0.07886972,0.02426317],"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":[0.001025979,0.0006368685,0.01208522,0.0006484904,0.0002988371,0.0007878167,0.003839736,0.006771079,0.0112153,0.2486909,0.3973485,0.3166512],"study_design_scores_gemma":[0.0003204582,0.0003833636,0.003560088,0.0007091631,0.00009532737,0.0003682318,0.0009256268,0.01486944,0.007937455,0.1009304,0.8697323,0.0001681811],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02108044,0.00136536,0.7404872,0.1351431,0.004532753,0.005669244,0.01224667,0.02156762,0.05790772],"genre_scores_gemma":[0.04196206,0.0005909335,0.8921456,0.01163179,0.0005602529,0.003083688,0.03322035,0.003716644,0.01308862],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9892511,"threshold_uncertainty_score":0.9938304,"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."}}