{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.2498929,0.001918886,0.002685433,0.02983064,0.002360534,0.01317186,0.002209963,0.003271524,0.004057144],"category_scores_gemma":[0.4847267,0.0005589587,0.003381252,0.02433532,0.004194131,0.009072207,0.006845463,0.001316474,0.0007206557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01303487,"about_ca_system_score_gemma":0.010542,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003381289,"about_ca_topic_score_gemma":0.003936204,"domain_scores_codex":[0.6204483,0.2391909,0.04481691,0.008143893,0.08410408,0.003295944],"domain_scores_gemma":[0.3892099,0.426066,0.04134883,0.01953878,0.1194834,0.004353078],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.006983816,0.001582761,0.1458545,0.02292278,0.008562799,0.0007864989,0.008850274,0.02599491,0.006801965,0.1131825,0.0117153,0.6467619],"study_design_scores_gemma":[0.00278488,0.01102493,0.2527389,0.02037111,0.02324738,0.001499499,0.03400346,0.1113592,0.0681212,0.2866514,0.1871701,0.001028041],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5546474,0.05411362,0.1926326,0.02112922,0.001888275,0.0129339,0.0106283,0.0009118502,0.1511147],"genre_scores_gemma":[0.8184204,0.006801391,0.1611646,0.001066737,0.0003659591,0.005573158,0.003717203,0.0001946179,0.002695877],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.750107,"threshold_uncertainty_score":0.9250156,"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."}}