{"id":"W4413109704","doi":"10.1093/bioadv/vbaf131","title":"Next generation biobanking ontology: introducing–omics contextual data to biobanking ontology","year":2024,"lang":"en","type":"article","venue":"Bioinformatics Advances","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Genome British Columbia; Simon Fraser University","funders":"King Fahad Medical City","keywords":"Biobank; Ontology; Computer science; Data science; Ontology-based data integration; Data discovery; Open Biomedical Ontologies; Data integration; Information retrieval; Metadata; Data management; World Wide Web; Data mining; Semantic Web; 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":[],"consensus_categories":[],"category_scores_codex":[0.0005437962,0.0002864954,0.000313205,0.0001703409,0.0001809666,0.0002206142,0.0007674654,0.0003025139,0.00002259681],"category_scores_gemma":[0.0006406454,0.000243414,0.00007075524,0.0002532451,0.000236287,0.00006668218,0.0005541632,0.0002052381,0.00009513434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004129038,"about_ca_system_score_gemma":0.0001701561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002729,"about_ca_topic_score_gemma":0.0004079971,"domain_scores_codex":[0.9980445,0.00005112362,0.0005953862,0.0005985062,0.0002129347,0.0004975477],"domain_scores_gemma":[0.9987134,0.00007022211,0.0001238714,0.0008758704,0.00008246901,0.0001341636],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007183429,0.00004443726,0.0003597217,0.0001995622,0.0001483251,0.00001489878,0.0008281247,0.0001711049,0.0911022,0.001477534,0.02655707,0.8790252],"study_design_scores_gemma":[0.000335254,0.0005063696,0.00008918699,0.0001007679,0.00004953945,0.0001132577,0.0008563268,0.04297824,0.0136824,0.0001678853,0.9406331,0.0004877217],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5096661,0.06994753,0.3976127,0.006991413,0.009896616,0.001117634,0.0006893561,0.0006539545,0.003424737],"genre_scores_gemma":[0.7586837,0.003370181,0.2281522,0.003389807,0.002968123,0.00005523481,0.002875109,0.00006517613,0.0004404672],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.914076,"threshold_uncertainty_score":0.9926136,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08394483396799426,"score_gpt":0.3364466195746183,"score_spread":0.2525017856066241,"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."}}