{"id":"W4376104255","doi":"10.1101/2023.05.09.539725","title":"NGBO: Introducing -omics metadata to biobanking ontology","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Genome British Columbia; Simon Fraser University; University of British Columbia","funders":"","keywords":"Biobank; Ontology; Computer science; Metadata; Data science; Discoverability; Open Biomedical Ontologies; Ontology-based data integration; Data integration; Information retrieval; Data management; World Wide Web; Semantic Web; Data mining; Suggested Upper Merged Ontology; Bioinformatics","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":[],"consensus_categories":[],"category_scores_codex":[0.01210872,0.0007642945,0.000592828,0.005204411,0.001970647,0.005479038,0.002606057,0.001871483,0.003823973],"category_scores_gemma":[0.01243633,0.000782426,0.002019926,0.003879607,0.00216952,0.01001078,0.007087282,0.004054057,0.002259169],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004669263,"about_ca_system_score_gemma":0.009244273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02230467,"about_ca_topic_score_gemma":0.02283867,"domain_scores_codex":[0.9941736,0.001728235,0.001084094,0.0006945584,0.001916649,0.0004029485],"domain_scores_gemma":[0.9926693,0.002360217,0.0005457255,0.001819257,0.002059592,0.0005457647],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001469397,0.0002877935,0.004747285,0.001593658,0.0001300772,0.001377649,0.004253493,0.01048955,0.01099561,0.6998501,0.08698095,0.1791468],"study_design_scores_gemma":[0.00003313505,0.00002173949,0.001045852,0.0008813013,0.00005499716,0.0003886704,0.0007424543,0.0283552,0.004960334,0.1161272,0.8473098,0.00007936572],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004230816,0.0004928333,0.9557475,0.005434804,0.000615237,0.0009635488,0.006301331,0.009193573,0.01702042],"genre_scores_gemma":[0.02765575,0.0008947714,0.9468501,0.002175287,0.0001810528,0.0008516279,0.01508067,0.001491538,0.004819307],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02230467,"threshold_uncertainty_score":0.06403774,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02716611054077953,"score_gpt":0.2609125674132383,"score_spread":0.2337464568724587,"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."}}