{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03189269,0.001624679,0.0008299806,0.006366481,0.003170835,0.00745253,0.004559487,0.005394112,0.004435079],"category_scores_gemma":[0.09236216,0.002269349,0.001509631,0.003806267,0.008380885,0.01724153,0.005264744,0.004170153,0.002562949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002934493,"about_ca_system_score_gemma":0.005337317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005035055,"about_ca_topic_score_gemma":0.01134005,"domain_scores_codex":[0.9747638,0.01546066,0.004549985,0.0009441189,0.003632754,0.0006487575],"domain_scores_gemma":[0.912922,0.04909393,0.005867164,0.01135255,0.01919332,0.001571048],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000112264,0.0006182034,0.00222415,0.002014965,0.00005861257,0.0006166266,0.02324392,0.01097221,0.01193784,0.7229789,0.02664894,0.1985733],"study_design_scores_gemma":[0.0002068304,0.000185408,0.001599374,0.003262051,0.00009335337,0.0007937746,0.01056321,0.03188361,0.01351183,0.6068211,0.330854,0.0002254766],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006109477,0.0005668121,0.9625984,0.005385649,0.0001373526,0.002448321,0.0001700997,0.001884621,0.02069929],"genre_scores_gemma":[0.02389963,0.000373868,0.9724125,0.0003679197,0.00002192037,0.001399616,0.0001989894,0.0001682829,0.001157132],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03189269,"threshold_uncertainty_score":0.1686667,"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."}}