{"id":"W206208155","doi":"10.2307/41409965","title":"Guidelines for Designing Visual Ontologies to Support Knowledge Identification1","year":2011,"lang":"en","type":"article","venue":"MIS Quarterly","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":102,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Luonnontieteiden ja Tekniikan Tutkimuksen Toimikunta; Texas A and M International University; University of British Columbia; Texas A and M University","keywords":"Identification (biology); Knowledge management; Computer science; Visual analytics; Data science; Visualization; Data mining","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004707885,0.0001530923,0.0002033044,0.000137255,0.0001395595,0.0001531641,0.0009769708,0.00006542938,0.00002434965],"category_scores_gemma":[0.0002238241,0.0001330068,0.00008923007,0.0001945203,0.00003622359,0.0003774407,0.00004108391,0.00004200325,0.0003383138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002233491,"about_ca_system_score_gemma":0.00007906719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009210368,"about_ca_topic_score_gemma":0.0002192973,"domain_scores_codex":[0.9986533,0.00004157059,0.000394933,0.0004376978,0.0001252893,0.0003471891],"domain_scores_gemma":[0.9987474,0.0001364733,0.00008478168,0.0004797821,0.0004613485,0.00009023146],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003309241,0.0002012454,0.001131361,0.00005017137,0.00004628746,0.00001542603,0.03876299,0.000001593606,0.00857097,0.02119039,0.1052058,0.8247907],"study_design_scores_gemma":[0.005957797,0.02856696,0.1851086,0.000440993,0.0002874333,0.0003106385,0.04615255,0.05005724,0.3291687,0.1390311,0.2088722,0.006045843],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02377302,0.0001377414,0.9712672,0.0009728295,0.0008322695,0.0003209263,0.000001881784,0.0003928114,0.002301367],"genre_scores_gemma":[0.5759226,0.000001543278,0.4217683,0.0004589873,0.0001236436,0.0001496523,0.000003634146,0.00001143398,0.001560244],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8187448,"threshold_uncertainty_score":0.5423859,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2368245507202791,"score_gpt":0.3915677898257199,"score_spread":0.1547432391054408,"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."}}