{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001052207,0.0006642086,0.0007596283,0.0003292175,0.0001900968,0.0002455675,0.001306419,0.001259643,0.00001340137],"category_scores_gemma":[0.001814122,0.0006928868,0.000229052,0.000464937,0.0002334553,0.00000902841,0.002452902,0.0007738111,0.0001425857],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001191916,"about_ca_system_score_gemma":0.0005563321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001429265,"about_ca_topic_score_gemma":0.00003941885,"domain_scores_codex":[0.995955,0.0001866017,0.0006598085,0.001927809,0.0003460851,0.0009246238],"domain_scores_gemma":[0.9965929,0.00006358531,0.0003225389,0.002322142,0.0003126486,0.000386228],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007068072,0.00007630548,0.003056199,0.0001974887,0.0003903907,0.00006114411,0.00001128155,0.0001421626,0.987106,0.0002130076,0.008620758,0.00005458346],"study_design_scores_gemma":[0.0007649761,0.0003827297,0.03213431,0.000397804,0.0002745355,1.744728e-7,0.00001893156,0.0001980639,0.7452638,0.00001392381,0.2185589,0.001991895],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9499258,0.00270538,0.03763894,0.002610407,0.005027681,0.000772139,0.000623521,0.0006848475,0.00001131953],"genre_scores_gemma":[0.948963,0.0005731778,0.04639571,0.00119211,0.002293192,0.0002389086,0.00001041786,0.0002417835,0.00009174151],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2418422,"threshold_uncertainty_score":0.9995522,"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."}}