{"id":"W4213288981","doi":"10.1093/gigascience/giac003","title":"Future-proofing and maximizing the utility of metadata: The PHA4GE SARS-CoV-2 contextual data specification package","year":2022,"lang":"en","type":"article","venue":"GigaScience","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Dalhousie University; McMaster University; BC Centre for Disease Control; Simon Fraser University","funders":"Biotechnology and Biological Sciences Research Council; National Institutes of Health; U.S. National Library of Medicine; Wellcome Trust; Bill and Melinda Gates Foundation","keywords":"Interoperability; Computer science; Metadata; Harmonization; Standardization; Consistency (knowledge bases); Data science; Open science; Data sharing; Openness to experience; Best practice; Data integration; World Wide Web; Data mining; Medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.04289045,0.00148209,0.0009386878,0.004534183,0.001814669,0.00667534,0.004109139,0.002462017,0.007603279],"category_scores_gemma":[0.05810809,0.001534492,0.00276845,0.003524839,0.00230259,0.008320876,0.009239226,0.004102416,0.007781916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002625597,"about_ca_system_score_gemma":0.0127337,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01091644,"about_ca_topic_score_gemma":0.009678124,"domain_scores_codex":[0.9800053,0.007406828,0.004116584,0.001916808,0.005532141,0.001022352],"domain_scores_gemma":[0.9469278,0.01517876,0.003444731,0.02203972,0.01032851,0.002080437],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001014749,0.000519371,0.01968648,0.001942043,0.0002988145,0.001081809,0.003672401,0.01515251,0.01874776,0.3520587,0.3496129,0.2362125],"study_design_scores_gemma":[0.0001691798,0.000152785,0.003248392,0.001344132,0.00012935,0.0005897633,0.0008139704,0.02333118,0.02013559,0.1130496,0.8367696,0.000266373],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005897163,0.0003195245,0.8995104,0.005690417,0.0005086298,0.001539562,0.02636385,0.04826951,0.01190094],"genre_scores_gemma":[0.03017984,0.0006250709,0.873062,0.002500233,0.0002459393,0.001719662,0.07592403,0.01089586,0.004847391],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9571096,"threshold_uncertainty_score":0.2268291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.102220590335467,"score_gpt":0.3214307627217884,"score_spread":0.2192101723863215,"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."}}