{"id":"W2887921957","doi":"10.1007/s00439-018-1923-y","title":"Introduction: the why and whither of genomic data sharing","year":2018,"lang":"en","type":"editorial","venue":"Human Genetics","topic":"Ethics in Clinical Research","field":"Medicine","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; McGill Genome Centre; McGill University Health Centre","funders":"Canadian Institutes of Health Research; WYNG Foundation; Genome Canada","keywords":"Biology; Human genetics; Data sharing; Metabolic disease; Genome Biology; Computational biology; Genetics; Evolutionary biology; Genomics; Genome; Gene; Endocrinology","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"],"consensus_categories":[],"category_scores_codex":[0.02982035,0.003181233,0.004732064,0.005034709,0.007908572,0.0160618,0.006257528,0.0565201,0.01274717],"category_scores_gemma":[0.1346364,0.001868324,0.004029237,0.00310991,0.01188978,0.01168919,0.005358557,0.05913623,0.006331768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00895539,"about_ca_system_score_gemma":0.01104277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00415346,"about_ca_topic_score_gemma":0.01031098,"domain_scores_codex":[0.969524,0.0108266,0.004239052,0.003687732,0.00999142,0.001731101],"domain_scores_gemma":[0.8271884,0.1223761,0.00939906,0.004037827,0.02631089,0.01068768],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001152015,0.000004768568,0.00002256592,0.0001732064,0.00002227613,0.00007923048,0.00005216021,0.00001416509,0.00001467687,0.001125662,0.9959942,0.002485563],"study_design_scores_gemma":[0.00006706097,0.00001756679,0.0002273532,0.001707514,0.00009046108,0.00034986,0.0001642779,0.0001428735,0.00005540347,0.005316168,0.9918157,0.00004570458],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.00002136958,0.006036528,0.0001617534,0.2178995,0.7750495,0.000008458135,0.00003302455,0.0000301354,0.0007597097],"genre_scores_gemma":[0.0003053766,0.002120681,0.0001268775,0.1300445,0.8652129,0.00001773639,0.00001424846,0.00003582958,0.00212195],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.9701797,"threshold_uncertainty_score":0.157707,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4723638257086499,"score_gpt":0.5537459159488258,"score_spread":0.0813820902401759,"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."}}