{"id":"W3176752316","doi":"10.1093/database/baab035","title":"Which methods are the most effective in enabling novice users to participate in ontology creation? A usability study","year":2021,"lang":"en","type":"article","venue":"Database","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Government of Canada; Agriculture and Agri-Food Canada; University of Manitoba","funders":"Canadian Institutes of Health Research; National Science Foundation","keywords":"Usability; Wizard; Computer science; Ontology; Interoperability; World Wide Web; Set (abstract data type); Think aloud protocol; Data curation; Data science; Information retrieval; Human–computer interaction","routes":{"ca_aff":true,"ca_fund":true,"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":[0.04979377,0.001453377,0.001283268,0.00274489,0.001405875,0.004731631,0.00117979,0.002146872,0.001551328],"category_scores_gemma":[0.1833283,0.0009693285,0.001206735,0.001521033,0.001421697,0.005027238,0.002067712,0.0009613841,0.0005276149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001156063,"about_ca_system_score_gemma":0.001308629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001533451,"about_ca_topic_score_gemma":0.003114467,"domain_scores_codex":[0.9609003,0.02576671,0.00517904,0.00281898,0.004163184,0.001171816],"domain_scores_gemma":[0.630496,0.3292925,0.009905518,0.009320769,0.01830075,0.002684394],"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":[0.009032154,0.005492121,0.1490852,0.01400615,0.0009158808,0.0009372762,0.09004281,0.001733103,0.03583985,0.001457475,0.006189215,0.6852688],"study_design_scores_gemma":[0.005735212,0.05419359,0.5214074,0.008442292,0.004615962,0.004356417,0.1614803,0.05507306,0.09001995,0.01146083,0.08078992,0.002425167],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9641719,0.001965568,0.02589959,0.000875129,0.00009146136,0.001775212,0.0002343046,0.0008941712,0.004092736],"genre_scores_gemma":[0.8887318,0.001378361,0.1045211,0.0005424241,0.00005946144,0.002680618,0.0003520641,0.0004391161,0.001294996],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04979377,"threshold_uncertainty_score":0.2633377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03992296776808297,"score_gpt":0.4024975044328333,"score_spread":0.3625745366647504,"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."}}