{"id":"W3134184401","doi":"10.1038/s41588-021-00783-5","title":"The Polygenic Score Catalog as an open database for reproducibility and systematic evaluation","year":2021,"lang":"en","type":"article","venue":"Nature Genetics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":744,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Health Data Research UK; National Human Genome Research Institute; Medical Research Council; Engineering and Physical Sciences Research Council; Canadian Institutes of Health Research; National Institute for Health and Care Research; Baker IDI Heart and Diabetes Institute; European Bioinformatics Institute; Wellcome Trust; British Heart Foundation; Government of Canada","keywords":"Biology; Metadata; Resource (disambiguation); Computational biology; Bioinformatics; Computer science; World Wide Web; Information retrieval; Database","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","open_science"],"consensus_categories":[],"category_scores_codex":[0.04087165,0.001736747,0.003799905,0.02738717,0.001956834,0.008521655,0.006522508,0.002296967,0.03467685],"category_scores_gemma":[0.1789403,0.001738155,0.002232207,0.02531439,0.001128347,0.005537472,0.01053026,0.00331547,0.02282583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001415206,"about_ca_system_score_gemma":0.01276267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006283551,"about_ca_topic_score_gemma":0.007534715,"domain_scores_codex":[0.9733065,0.006596501,0.008661466,0.003806996,0.006848053,0.0007804498],"domain_scores_gemma":[0.7640381,0.07802505,0.0167111,0.1005927,0.03146275,0.009170249],"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":[0.001752657,0.0006602383,0.03614425,0.004851955,0.002473219,0.0005390605,0.0009782634,0.002137618,0.003844315,0.04990031,0.5939721,0.302746],"study_design_scores_gemma":[0.001220762,0.0003148033,0.06274535,0.002527795,0.001806766,0.001336384,0.0004430471,0.01217378,0.00713794,0.1015978,0.808098,0.0005976164],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.01346913,0.002859141,0.2377306,0.002484913,0.001211549,0.001794793,0.6288915,0.09524836,0.01631],"genre_scores_gemma":[0.03498307,0.001623012,0.2284443,0.001309932,0.0005124146,0.003494984,0.7120816,0.01336272,0.00418794],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9934775,"threshold_uncertainty_score":0.2161525,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03726199298882381,"score_gpt":0.3643421259135172,"score_spread":0.3270801329246934,"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."}}