{"id":"W4200292339","doi":"10.3390/info13010008","title":"Interfaces for Searching and Triaging Large Document Sets: An Ontology-Supported Visual Analytics Approach","year":2021,"lang":"en","type":"article","venue":"Information","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Ontology; Visual analytics; Triage; Workflow; Analytics; Interface (matter); Information retrieval; Set (abstract data type); Domain (mathematical analysis); Data science; World Wide Web; Visualization; Data mining; Database","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":[0.0005317862,0.00007723446,0.0001106458,0.0001288335,0.000138336,0.0006141651,0.0001856283,0.00003846388,0.000007565367],"category_scores_gemma":[0.0001015399,0.00007404977,0.00002204373,0.0002292484,0.00001400485,0.003507809,0.0001524039,0.00006247315,0.000005918723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000274154,"about_ca_system_score_gemma":0.00007462047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005521509,"about_ca_topic_score_gemma":0.00001039214,"domain_scores_codex":[0.9991904,0.00004769291,0.0002916261,0.0001310178,0.0001633057,0.0001759352],"domain_scores_gemma":[0.9994482,0.00003907323,0.000112586,0.0001807081,0.0001482166,0.00007116981],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006645893,0.0004722379,0.00335818,0.0006133282,0.0002201773,0.000007069869,0.03016168,0.003404815,0.0003193283,0.5377458,0.004541699,0.4190892],"study_design_scores_gemma":[0.00060374,0.0000602128,0.0002927269,0.00001035624,0.000009944326,0.00001243338,0.001196237,0.9866141,0.0006817097,0.0008258576,0.009593745,0.00009894989],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01882155,0.00001459884,0.9800132,0.0002927626,0.00008290292,0.0001258166,0.00001986328,0.00007327499,0.0005560331],"genre_scores_gemma":[0.9224663,0.00002030483,0.0748961,0.001375107,0.000026859,0.00001203475,0.001114554,0.000004798479,0.00008388073],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9832093,"threshold_uncertainty_score":0.5922408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03007795530913903,"score_gpt":0.3509685039729982,"score_spread":0.3208905486638591,"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."}}