{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01076042,0.001209298,0.0009077451,0.003973006,0.001188561,0.01090382,0.004490122,0.00196957,0.006610609],"category_scores_gemma":[0.02804163,0.001056536,0.001822506,0.002176081,0.002851246,0.01048716,0.004385065,0.002409593,0.002162272],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001497177,"about_ca_system_score_gemma":0.001935301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003563934,"about_ca_topic_score_gemma":0.00337825,"domain_scores_codex":[0.9951002,0.002207415,0.0004958587,0.0007584034,0.00115834,0.0002797201],"domain_scores_gemma":[0.9814308,0.01059139,0.0009202088,0.003339433,0.002775673,0.0009425283],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001305008,0.0006676009,0.003970069,0.002480158,0.0002130724,0.0008604066,0.02886124,0.02122762,0.0571145,0.2003709,0.02959356,0.6533359],"study_design_scores_gemma":[0.0003477069,0.0008931853,0.002979268,0.001799581,0.0002420302,0.001391982,0.009178901,0.3942665,0.06729246,0.2619286,0.2591867,0.0004929776],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006734429,0.0002644215,0.9836204,0.0008634064,0.00003698841,0.0003332184,0.0002483411,0.004452054,0.003446663],"genre_scores_gemma":[0.05198989,0.0003384766,0.9440031,0.0002761622,0.00003355824,0.0004041066,0.0005004803,0.0005989982,0.001855158],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01090382,"threshold_uncertainty_score":0.05690724,"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."}}