{"id":"W2110301462","doi":"10.1093/bib/bbs053","title":"Evaluation of research in biomedical ontologies","year":2012,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":81,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Association of Occupational Therapists; Carleton University","funders":"National Human Genome Research Institute","keywords":"Computer science; Ontology; Open Biomedical Ontologies; Terminology; Biomedicine; IDEF5; Data science; Consistency (knowledge bases); Controlled vocabulary; Domain (mathematical analysis); Information retrieval; Semantic Web; Upper ontology; Ontology alignment; Artificial intelligence; 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":[0.006735094,0.0000826672,0.0001514583,0.0002457269,0.00002494507,0.000007842747,0.0001978016,0.0002637685,0.00001213739],"category_scores_gemma":[0.002229171,0.00007108516,0.00003055374,0.0003827781,0.0004381651,0.000009179535,0.0001518772,0.0001824063,0.000008280867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005044845,"about_ca_system_score_gemma":0.0001642976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001322254,"about_ca_topic_score_gemma":0.00006546977,"domain_scores_codex":[0.998179,0.0001738569,0.0004303403,0.00009805262,0.0007151317,0.0004036466],"domain_scores_gemma":[0.9993964,0.00008563573,0.00008209162,0.0002154231,0.0001607088,0.0000596697],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001185693,0.0008264919,0.08101552,0.0003785261,0.00005403538,0.000002585225,0.005173848,0.00005629221,0.0106641,0.001007229,0.01312209,0.8875807],"study_design_scores_gemma":[0.01143028,0.002276121,0.5505826,0.001414109,0.0001064162,0.0001564713,0.02143929,0.03476217,0.08336408,0.007146527,0.2858112,0.001510736],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9919325,0.001949756,0.0005101322,0.0005715783,0.0001613657,0.0002174133,0.000005705026,0.00001202441,0.004639541],"genre_scores_gemma":[0.9892923,0.000138842,0.01031925,0.0001104444,0.00006090214,0.00002539513,0.00002980774,0.00000542429,0.00001761538],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.88607,"threshold_uncertainty_score":0.2898768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1457497501625115,"score_gpt":0.4224649152567926,"score_spread":0.2767151650942811,"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."}}